{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T12:06:22Z","timestamp":1777896382864,"version":"3.51.4"},"reference-count":175,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSC 113-2410-H-167-012-MY3"],"award-info":[{"award-number":["NSC 113-2410-H-167-012-MY3"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSC 113-2410-H-167-012-MY3"],"award-info":[{"award-number":["NSC 113-2410-H-167-012-MY3"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01201-x","type":"journal-article","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T08:15:48Z","timestamp":1751616948000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations"],"prefix":"10.1186","volume":"12","author":[{"given":"Sumit Singh","family":"Dhanda","sequence":"first","affiliation":[]},{"given":"Deepak","family":"Panwar","sequence":"additional","affiliation":[]},{"given":"Chia-Chen","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Tarun Kumar","family":"Sharma","sequence":"additional","affiliation":[]},{"given":"Deependra","family":"Rastogi","sequence":"additional","affiliation":[]},{"given":"Shantanu","family":"Bindewari","sequence":"additional","affiliation":[]},{"given":"Anand","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Yung-Hui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Neha","family":"Agarwal","sequence":"additional","affiliation":[]},{"given":"Saurabh","family":"Agarwal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,4]]},"reference":[{"key":"1201_CR1","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1038\/s41591-024-03098-0","volume":"30","author":"MC Roberts","year":"2024","unstructured":"Roberts MC, Holt KE, Del Fiol G, et al. Precision public health in the era of genomics and big data. Nature Med. 2024;30:1865\u201373. https:\/\/doi.org\/10.1038\/s41591-024-03098-0.","journal-title":"Nature Med"},{"issue":"11","key":"1201_CR2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002692","volume":"15","author":"H Ashrafian","year":"2018","unstructured":"Ashrafian H, Darzi A. Transforming health policy through machine learning. PLoS Med. 2018;15(11): e1002692. https:\/\/doi.org\/10.1371\/journal.pmed.1002692. (PMID:30422977;PMCID:PMC6233911).","journal-title":"PLoS Med"},{"issue":"13","key":"1201_CR3","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jama.2017.18391","volume":"319","author":"AL Beam","year":"2018","unstructured":"Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317\u20138. https:\/\/doi.org\/10.1001\/jama.2017.18391.","journal-title":"JAMA"},{"issue":"1","key":"1201_CR4","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ, et al. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44\u201356. https:\/\/doi.org\/10.1038\/s41591-018-0300-7.","journal-title":"Nat Med"},{"issue":"7639","key":"1201_CR5","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115\u20138. https:\/\/doi.org\/10.1038\/nature21056.","journal-title":"Nature"},{"key":"1201_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/srep26094","author":"R Miotto","year":"2016","unstructured":"Miotto R, et al. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. 2016. https:\/\/doi.org\/10.1038\/srep26094.","journal-title":"Sci Rep"},{"issue":"20","key":"1201_CR7","doi-asserted-by":"publisher","first-page":"8002","DOI":"10.3390\/s22208002","volume":"22","author":"J-D Huang","year":"2022","unstructured":"Huang J-D, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review. Sensors. 2022;22(20):8002. https:\/\/doi.org\/10.3390\/s22208002.","journal-title":"Sensors"},{"key":"1201_CR8","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s00500-023-09422-z","volume":"28","author":"Z Zhang","year":"2024","unstructured":"Zhang Z. Early warning model of adolescent mental health based on big data and machine learning. Soft Comput. 2024;28:811\u201328. https:\/\/doi.org\/10.1007\/s00500-023-09422-z.","journal-title":"Soft Comput"},{"issue":"24","key":"1201_CR9","doi-asserted-by":"publisher","first-page":"21959","DOI":"10.1109\/JIOT.2023.3313158","volume":"10","author":"Z Jiang","year":"2023","unstructured":"Jiang Z, Van Zoest V, Deng W, Ngai ECH, Liu J. Leveraging machine learning for disease diagnoses based on wearable devices: a survey. IEEE Internet Things J. 2023;10(24):21959\u201381. https:\/\/doi.org\/10.1109\/JIOT.2023.3313158.","journal-title":"IEEE Internet Things J"},{"key":"1201_CR10","doi-asserted-by":"publisher","first-page":"23366","DOI":"10.1109\/ACCESS.2023.3253885","volume":"11","author":"H Yang","year":"2023","unstructured":"Yang H, Chen Z, Yang H, Tian M. Predicting coronary heart disease using an improved LightGBM model: performance analysis and comparison. IEEE Access. 2023;11:23366\u201380. https:\/\/doi.org\/10.1109\/ACCESS.2023.3253885.","journal-title":"IEEE Access"},{"key":"1201_CR11","doi-asserted-by":"publisher","first-page":"49574","DOI":"10.1109\/ACCESS.2020.2979859","volume":"8","author":"X Li","year":"2020","unstructured":"Li X, Xu X, Wang J, Li J, Qin S, Yuan J. Study on prediction model of HIV incidence based on GRU neural network optimized by MHPSO. IEEE Access. 2020;8:49574\u201383. https:\/\/doi.org\/10.1109\/ACCESS.2020.2979859.","journal-title":"IEEE Access"},{"issue":"10","key":"1201_CR12","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TBME.2019.2897285","volume":"66","author":"C Davi","year":"2019","unstructured":"Davi C, et al. Severe dengue prognosis using human genome data and machine learning. IEEE Trans Biomed Eng. 2019;66(10):2861\u20138. https:\/\/doi.org\/10.1109\/TBME.2019.2897285.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"1","key":"1201_CR13","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/TCBB.2018.2849968","volume":"16","author":"Z Zeng","year":"2019","unstructured":"Zeng Z, Deng Y, Li X, Naumann T, Luo Y. Natural language processing for EHR-based computational phenotyping. IEEE\/ACM Trans Comput Biol Bioinf. 2019;16(1):139\u201353. https:\/\/doi.org\/10.1109\/TCBB.2018.2849968.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"1201_CR14","doi-asserted-by":"publisher","first-page":"34153","DOI":"10.1109\/ACCESS.2020.2974887","volume":"8","author":"S Cui","year":"2020","unstructured":"Cui S, Li C, Chen Z, Wang J, Yuan J. Research on risk prediction of dyslipidemia in steel workers based on recurrent neural network and LSTM neural network. IEEE Access. 2020;8:34153\u201361. https:\/\/doi.org\/10.1109\/ACCESS.2020.2974887.","journal-title":"IEEE Access"},{"key":"1201_CR15","doi-asserted-by":"publisher","first-page":"118198","DOI":"10.1109\/ACCESS.2022.3220329","volume":"10","author":"A Imran","year":"2022","unstructured":"Imran A, Nasir A, Bilal M, Sun G, Alzahrani A, Almuhaimeed A. Skin cancer detection using combined decision of deep learners. IEEE Access. 2022;10:118198\u2013212. https:\/\/doi.org\/10.1109\/ACCESS.2022.3220329.","journal-title":"IEEE Access"},{"key":"1201_CR16","doi-asserted-by":"publisher","first-page":"55663","DOI":"10.1109\/ACCESS.2021.3063944","volume":"9","author":"P Liu","year":"2021","unstructured":"Liu P, Jin K, Jiao Y, He M, Fei S. Prediction of second primary lung cancer patient\u2019s survivability based on improved eigenvector centrality-based feature selection. IEEE Access. 2021;9:55663\u201372. https:\/\/doi.org\/10.1109\/ACCESS.2021.3063944.","journal-title":"IEEE Access"},{"issue":"3","key":"1201_CR17","doi-asserted-by":"publisher","first-page":"525","DOI":"10.26599\/TST.2022.9010010","volume":"28","author":"X Lin","year":"2023","unstructured":"Lin X, et al. A case-finding clinical decision support system to identify subjects with chronic obstructive pulmonary disease based on public health data. Tsinghua Sci Technol. 2023;28(3):525\u201340. https:\/\/doi.org\/10.26599\/TST.2022.9010010.","journal-title":"Tsinghua Sci Technol"},{"key":"1201_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3430226S","author":"H Yang","year":"2024","unstructured":"Yang H, Chang J, He W, Wee CF, Yit JS, Feng M. Frailty modeling using machine learning methodologies: a systematic review with discussions on outstanding questions. IEEE J Biomed Health Inform. 2024. https:\/\/doi.org\/10.1109\/JBHI.2024.3430226S.","journal-title":"IEEE J Biomed Health Inform"},{"key":"1201_CR19","doi-asserted-by":"publisher","first-page":"157337","DOI":"10.1109\/ACCESS.2021.3131128","volume":"9","author":"H Siddiqui","year":"2021","unstructured":"Siddiqui H, et al. A survey on machine and deep learning models for childhood and adolescent obesity. IEEE Access. 2021;9:157337\u201360. https:\/\/doi.org\/10.1109\/ACCESS.2021.3131128.","journal-title":"IEEE Access"},{"key":"1201_CR20","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.1109\/TNSRE.2022.3188560","volume":"30","author":"M Huang","year":"2022","unstructured":"Huang M, et al. Joint-channel-connectivity-based feature selection and classification on fNIRS for stress detection in decision-making. IEEE Trans Neural Syst Rehabil Eng. 2022;30:1858\u201369. https:\/\/doi.org\/10.1109\/TNSRE.2022.3188560.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"1201_CR21","doi-asserted-by":"publisher","unstructured":"Chen LC, Sheu JT, Chuang YJ, Tsao Y. Predicting the travel distance of patients to access healthcare using deep neural networks. IEEE J Transl Eng Health Med. 2022;10:1\u201311, 2022, Art no. 4900411, https:\/\/doi.org\/10.1109\/JTEHM.2021.3134106.","DOI":"10.1109\/JTEHM.2021.3134106"},{"key":"1201_CR22","doi-asserted-by":"publisher","first-page":"42243","DOI":"10.1109\/ACCESS.2022.3168045","volume":"10","author":"E-E Etu","year":"2022","unstructured":"Etu E-E, et al. Prediction of length of stay in the emergency department for COVID-19 patients: a machine learning approach. IEEE Access. 2022;10:42243\u201351. https:\/\/doi.org\/10.1109\/ACCESS.2022.3168045.","journal-title":"IEEE Access"},{"key":"1201_CR23","doi-asserted-by":"publisher","first-page":"81252","DOI":"10.1109\/ACCESS.2024.3411162","volume":"12","author":"M Kumar","year":"2024","unstructured":"Kumar M, Kumar Singh S, Kim S. Predictive analytics for mortality: FSRNCA-FLANN modeling using public health inventory records. IEEE Access. 2024;12:81252\u201364. https:\/\/doi.org\/10.1109\/ACCESS.2024.3411162.","journal-title":"IEEE Access"},{"key":"1201_CR24","doi-asserted-by":"publisher","first-page":"68184","DOI":"10.1109\/ACCESS.2024.3399827","volume":"12","author":"IN Margret","year":"2024","unstructured":"Margret IN, Rajakumar K, Arulalan KV, Manikandan S. Statistical insights into machine learning-based box models for pregnancy care and maternal mortality reduction: a literature survey. IEEE Access. 2024;12:68184\u2013207. https:\/\/doi.org\/10.1109\/ACCESS.2024.3399827.","journal-title":"IEEE Access"},{"key":"1201_CR25","doi-asserted-by":"publisher","first-page":"89754","DOI":"10.1109\/ACCESS.2024.3418146","volume":"12","author":"M Venturini","year":"2024","unstructured":"Venturini M, Haredasht FN, Sabov\u010dik F, Miller RJH, Kuznetsova T, Vens C. Improving 1-year mortality prediction after pediatric heart transplantation using hypothetical donor-recipient matches. IEEE Access. 2024;12:89754\u201362. https:\/\/doi.org\/10.1109\/ACCESS.2024.3418146.","journal-title":"IEEE Access"},{"key":"1201_CR26","doi-asserted-by":"publisher","first-page":"11811","DOI":"10.1109\/ACCESS.2023.3242290","volume":"11","author":"S Bengesi","year":"2023","unstructured":"Bengesi S, Oladunni T, Olusegun R, Audu H. A machine learning-sentiment analysis on monkeypox outbreak: an extensive dataset to show the polarity of public opinion from twitter tweets. IEEE Access. 2023;11:11811\u201326. https:\/\/doi.org\/10.1109\/ACCESS.2023.3242290.","journal-title":"IEEE Access"},{"key":"1201_CR27","doi-asserted-by":"publisher","first-page":"134592","DOI":"10.1109\/ACCESS.2022.3232307","volume":"10","author":"U Ullah","year":"2022","unstructured":"Ullah U, Jurado AGO, Gonzalez ID, Garcia-Zapirain B. A fully connected quantum convolutional neural network for classifying ischemic cardiopathy. IEEE Access. 2022;10:134592\u2013605. https:\/\/doi.org\/10.1109\/ACCESS.2022.3232307.","journal-title":"IEEE Access"},{"key":"1201_CR28","doi-asserted-by":"publisher","first-page":"73029","DOI":"10.1109\/ACCESS.2021.3079182","volume":"9","author":"M Shokrekhodaei","year":"2021","unstructured":"Shokrekhodaei M, Cistola DP, Roberts RC, Quinones S. Non-invasive glucose monitoring using optical sensor and machine learning techniques for diabetes applications. IEEE Access. 2021;9:73029\u201345. https:\/\/doi.org\/10.1109\/ACCESS.2021.3079182.","journal-title":"IEEE Access"},{"key":"1201_CR29","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/RBME.2023.3242261","volume":"17","author":"HY Lu","year":"2024","unstructured":"Lu HY, et al. Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes. IEEE Rev Biomed Eng. 2024;17:98\u2013117. https:\/\/doi.org\/10.1109\/RBME.2023.3242261.","journal-title":"IEEE Rev Biomed Eng"},{"key":"1201_CR30","doi-asserted-by":"publisher","first-page":"90575","DOI":"10.1109\/ACCESS.2023.3307495","volume":"11","author":"MM Vlajnic","year":"2023","unstructured":"Vlajnic MM, Simske SJ. Accuracy and performance of machine learning methodologies: novel assessments of country pandemic vulnerability based on non-pandemic predictors. IEEE Access. 2023;11:90575\u201394. https:\/\/doi.org\/10.1109\/ACCESS.2023.3307495.","journal-title":"IEEE Access"},{"key":"1201_CR31","doi-asserted-by":"publisher","first-page":"67555","DOI":"10.1109\/ACCESS.2018.2879115","volume":"6","author":"SA Abbas","year":"2018","unstructured":"Abbas SA, Riaz R, Kazmi SZH, Rizvi SS, Kwon SJ. Cause analysis of caesarian sections and application of machine learning methods for classification of birth data. IEEE Access. 2018;6:67555\u201361. https:\/\/doi.org\/10.1109\/ACCESS.2018.2879115.","journal-title":"IEEE Access"},{"key":"1201_CR32","doi-asserted-by":"publisher","first-page":"36026","DOI":"10.1109\/ACCESS.2024.3373910","volume":"12","author":"N Sengupta","year":"2024","unstructured":"Sengupta N, Rao AS, Yan B, Palaniswami M. A survey of wearable sensors and machine learning algorithms for automated stroke rehabilitation. IEEE Access. 2024;12:36026\u201354. https:\/\/doi.org\/10.1109\/ACCESS.2024.3373910.","journal-title":"IEEE Access"},{"key":"1201_CR33","doi-asserted-by":"publisher","first-page":"31561","DOI":"10.1109\/ACCESS.2022.3160213","volume":"10","author":"E Elyan","year":"2022","unstructured":"Elyan E, Hussain A, Sheikh A, Elmanama AA, Vuttipittayamongkol P, Hijazi K. Antimicrobial resistance and machine learning: challenges and opportunities. IEEE Access. 2022;10:31561\u201377. https:\/\/doi.org\/10.1109\/ACCESS.2022.3160213.","journal-title":"IEEE Access"},{"issue":"4","key":"1201_CR34","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1109\/TCBB.2008.125","volume":"6","author":"AM Gonzalez","year":"2009","unstructured":"Gonzalez AM, Azuaje FJ, Ramirez JL, da Silveira JF, Dorronsoro JR. Machine learning techniques for the automated classification of adhesin-like proteins in the human protozoan parasite trypanosoma cruzi. IEEE\/ACM Trans Comput Biol Bioinform. 2009;6(4):695\u2013702. https:\/\/doi.org\/10.1109\/TCBB.2008.125.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"1201_CR35","doi-asserted-by":"publisher","first-page":"167605","DOI":"10.1109\/ACCESS.2019.2953920","volume":"7","author":"M Abdar","year":"2019","unstructured":"Abdar M, Acharya UR, Sarrafzadegan N, Makarenkov V. NE-nu-SVC: a new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease. IEEE Access. 2019;7:167605\u201320. https:\/\/doi.org\/10.1109\/ACCESS.2019.2953920.","journal-title":"IEEE Access"},{"key":"1201_CR36","doi-asserted-by":"publisher","first-page":"89098","DOI":"10.1109\/ACCESS.2024.3418974","volume":"12","author":"G Obaido","year":"2024","unstructured":"Obaido G, et al. An improved framework for detecting thyroid disease using filter-based feature selection and stacking ensemble. IEEE Access. 2024;12:89098\u2013112. https:\/\/doi.org\/10.1109\/ACCESS.2024.3418974.","journal-title":"IEEE Access"},{"key":"1201_CR37","doi-asserted-by":"publisher","unstructured":"Sobrinho A, Queiroz ACMDS, Dias Da Silva L, De Barros Costa E, Eliete Pinheiro M, Perkusich A. Computer-aided diagnosis of chronic kidney disease in developing countries: a comparative analysis of machine learning techniques. IEEE Access. 2020;8:25407\u201325419. https:\/\/doi.org\/10.1109\/ACCESS.2020.2971208.","DOI":"10.1109\/ACCESS.2020.2971208"},{"key":"1201_CR38","doi-asserted-by":"publisher","unstructured":"Rashed-Al-Mahfuz M, Haque A, Azad A, Alyami SA, Quinn JMW, Moni MA. Clinically applicable machine learning approaches to identify attributes of chronic kidney disease (CKD) for use in low-cost diagnostic screening. IEEE J Transl Eng Health Med. 2021;9:1\u201311. Art no. 4900511. https:\/\/doi.org\/10.1109\/JTEHM.2021.3073629.","DOI":"10.1109\/JTEHM.2021.3073629"},{"key":"1201_CR39","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/OJEMB.2023.3330292","volume":"5","author":"D Chapman","year":"2024","unstructured":"Chapman D, Strong C, Tiver KD, Dharmaprani D, Jenkins E, Ganesan AN. Infra-red imaging to detect respirator leak in healthcare workers during fit-testing clinic. IEEE Open J Eng Med Biol. 2024;5:198\u2013204. https:\/\/doi.org\/10.1109\/OJEMB.2023.3330292.","journal-title":"IEEE Open J Eng Med Biol"},{"key":"1201_CR40","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1109\/TNSRE.2024.3355299","volume":"32","author":"M Shang","year":"2024","unstructured":"Shang M, et al. Otago exercises monitoring for older adults by a single IMU and hierarchical machine learning models. IEEE Trans Neural Syst Rehabil Eng. 2024;32:462\u201371. https:\/\/doi.org\/10.1109\/TNSRE.2024.3355299.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"1201_CR41","doi-asserted-by":"publisher","first-page":"14778","DOI":"10.1109\/ACCESS.2023.3242234","volume":"11","author":"N Braig","year":"2023","unstructured":"Braig N, Benz A, Voth S, Breitenbach J, Buettner R. Machine learning techniques for sentiment analysis of COVID-19-related twitter data. IEEE Access. 2023;11:14778\u2013803. https:\/\/doi.org\/10.1109\/ACCESS.2023.3242234.","journal-title":"IEEE Access"},{"key":"1201_CR42","doi-asserted-by":"publisher","first-page":"72420","DOI":"10.1109\/ACCESS.2021.3079121","volume":"9","author":"MM Rahman","year":"2021","unstructured":"Rahman MM, Paul KC, Hossain MA, Ali GGMN, Rahman MS, Thill J-C. Machine learning on the COVID-19 pandemic, human mobility and air quality: a review. IEEE Access. 2021;9:72420\u201350. https:\/\/doi.org\/10.1109\/ACCESS.2021.3079121.","journal-title":"IEEE Access"},{"key":"1201_CR43","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s12916-022-02725-2","volume":"21","author":"A Bleichrodt","year":"2023","unstructured":"Bleichrodt A, Dahal S, Maloney K, et al. Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July\u2013October 2022. BMC Med. 2023;21:19. https:\/\/doi.org\/10.1186\/s12916-022-02725-2.","journal-title":"BMC Med"},{"issue":"10","key":"1201_CR44","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004513","volume":"11","author":"M Santillana","year":"2015","unstructured":"Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput Biol. 2015;11(10): e1004513. https:\/\/doi.org\/10.1371\/journal.pcbi.1004513.","journal-title":"PLoS Comput Biol"},{"key":"1201_CR45","doi-asserted-by":"publisher","first-page":"12760","DOI":"10.1038\/srep12760","volume":"5","author":"V Lampos","year":"2015","unstructured":"Lampos V, Miller AC, Crossan S, Stefansen C. Advances in nowcasting influenza-like illness rates using search query logs. Sci Rep. 2015;5:12760. https:\/\/doi.org\/10.1038\/srep12760.","journal-title":"Sci Rep"},{"issue":"3","key":"1201_CR46","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0231236","volume":"15","author":"F Petropoulos","year":"2020","unstructured":"Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19\u201d s. PLoS ONE. 2020;15(3): e0231236. https:\/\/doi.org\/10.1371\/journal.pone.0231236.","journal-title":"PLoS ONE"},{"issue":"6489","key":"1201_CR47","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1126\/science.aba9757","volume":"368","author":"M Chinazzi","year":"2020","unstructured":"Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, Vespignani A. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science. 2020;368(6489):395\u2013400. https:\/\/doi.org\/10.1126\/science.aba9757.","journal-title":"Science"},{"issue":"10","key":"1201_CR48","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0005973","volume":"11","author":"P Guo","year":"2017","unstructured":"Guo P, Liu T, Zhang Q, Wang L, Xiao J, Zhang Q, Ma W. Developing a dengue forecast model using machine learning: a case study in China. PLoS Negl Trop Dis. 2017;11(10): e0005973. https:\/\/doi.org\/10.1371\/journal.pntd.0005973.","journal-title":"PLoS Negl Trop Dis"},{"key":"1201_CR49","doi-asserted-by":"publisher","unstructured":"SarderF, Akter S, Akter S. Predicting Dengue Outbreak from Climate Data Using Machine Learning Algorithms. In: 2022 IEEE International Conference on Data Science and Information System (ICDSIS), Hassan, India, 2022. p. 1\u20136, https:\/\/doi.org\/10.1109\/ICDSIS55133.2022.9915862.","DOI":"10.1109\/ICDSIS55133.2022.9915862"},{"key":"1201_CR50","doi-asserted-by":"publisher","unstructured":"Alfred R, Obit JH. The roles of machine learning methods in limiting the spread of deadly diseases: a systematic review. Heliyon. 2021;7(6):e07371. https:\/\/doi.org\/10.1016\/j.heliyon.2021. e07371. Epub 2021 Jun 23. PMID: 34179541; PMCID: PMC8219638.","DOI":"10.1016\/j.heliyon.2021"},{"issue":"1","key":"1201_CR51","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3390\/healthcare10010085","volume":"10","author":"P Guleria","year":"2022","unstructured":"Guleria P, Ahmed S, Alhumam A, Srinivasu PN. Empirical study on classifiers for earlier prediction of COVID-19 infection cure and death rate in the Indian States. Healthcare (Basel). 2022;10(1):85. https:\/\/doi.org\/10.3390\/healthcare10010085. (PMID:35052249;PMCID:PMC8775063).","journal-title":"Healthcare (Basel)"},{"issue":"4","key":"1201_CR52","doi-asserted-by":"publisher","first-page":"295","DOI":"10.4161\/viru.24041","volume":"4","author":"CI Siettos","year":"2013","unstructured":"Siettos CI, Russo L. Mathematical modeling of infectious disease dynamics. Virulence. 2013;4(4):295\u2013306. https:\/\/doi.org\/10.4161\/viru.24041. (Epub 2013 Apr 3. PMID: 23552814; PMCID: PMC3710332).","journal-title":"Virulence."},{"issue":"10","key":"1201_CR53","doi-asserted-by":"publisher","DOI":"10.1128\/aac.00751-23","volume":"67","author":"LM Abbo","year":"2023","unstructured":"Abbo LM, Vasiliu-Feltes I. Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies. Antimicrob Agents Chemother. 2023;67(10): e0075123. https:\/\/doi.org\/10.1128\/aac.00751-23. (Epub 2023 Sep 19. PMID: 37724872; PMCID: PMC10583659).","journal-title":"Antimicrob Agents Chemother"},{"issue":"9","key":"1201_CR54","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0009686","volume":"15","author":"L Hussain-Alkhateeb","year":"2021","unstructured":"Hussain-Alkhateeb L, Rivera Ram\u00edrez T, Kroeger A, Gozzer E, Runge-Ranzinger S. Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review. PLoS Negl Trop Dis. 2021;15(9): e0009686. https:\/\/doi.org\/10.1371\/journal.pntd.0009686. (PMID:34529649;PMCID:PMC8445439).","journal-title":"PLoS Negl Trop Dis"},{"issue":"11","key":"1201_CR55","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007518","volume":"15","author":"P Rangarajan","year":"2019","unstructured":"Rangarajan P, Mody SK, Marathe M. Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data. PLoS Comput Biol. 2019;15(11): e1007518. https:\/\/doi.org\/10.1371\/journal.pcbi.1007518. (PMID:31751346;PMCID:PMC6894887).","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"1201_CR56","doi-asserted-by":"publisher","DOI":"10.1128\/aem.01292-23","volume":"90","author":"AH Buultjens","year":"2024","unstructured":"Buultjens AH, Vandelannoote K, Mercoulia K, Ballard S, Sloggett C, Howden BP, Seemann T, Stinear TP. High performance Legionella pneumophila source attribution using genomics-based machine learning classification. Appl Environ Microbiol. 2024;90(3): e0129223. https:\/\/doi.org\/10.1128\/aem.01292-23. (Epub 2024 Jan 30. PMID: 38289130; PMCID: PMC10952463).","journal-title":"Appl Environ Microbiol"},{"issue":"9","key":"1201_CR57","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.7393","volume":"19","author":"I Kagashe","year":"2017","unstructured":"Kagashe I, Yan Z, Suheryani I. Enhancing seasonal influenza surveillance: topic analysis of widely used medicinal drugs using twitter data. J Med Internet Res. 2017;19(9): e315. https:\/\/doi.org\/10.2196\/jmir.7393. (PMID:28899847;PMCID:PMC5617904).","journal-title":"J Med Internet Res"},{"issue":"6","key":"1201_CR58","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.3156","volume":"16","author":"E Yom-Tov","year":"2014","unstructured":"Yom-Tov E, Borsa D, Cox IJ, McKendry RA. Detecting disease outbreaks in mass gatherings using Internet data. J Med Internet Res. 2014;16(6): e154. https:\/\/doi.org\/10.2196\/jmir.3156. (PMID:24943128;PMCID:PMC4090384).","journal-title":"J Med Internet Res"},{"key":"1201_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103500","volume":"108","author":"A Gupta","year":"2020","unstructured":"Gupta A, Katarya R. Social media based surveillance systems for healthcare using machine learning: a systematic review. J Biomed Inform. 2020;108: 103500. https:\/\/doi.org\/10.1016\/j.jbi.2020.103500. (Epub 2020 Jul 2. PMID: 32622833; PMCID: PMC7331523).","journal-title":"J Biomed Inform"},{"key":"1201_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106817","volume":"158","author":"SA Rakhshan","year":"2023","unstructured":"Rakhshan SA, Nejad MS, Zaj M, Ghane FH. Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: a case study on COVID-19. Comput Biol Med. 2023;158: 106817. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106817. (Epub 2023 Mar 23. PMID: 36989749; PMCID: PMC10035804).","journal-title":"Comput Biol Med"},{"issue":"24","key":"1201_CR61","doi-asserted-by":"publisher","first-page":"9467","DOI":"10.3390\/ijerph17249467","volume":"17","author":"M Kim","year":"2020","unstructured":"Kim M, Chae K, Lee S, Jang HJ, Kim S. Automated classification of online sources for infectious disease occurrences using machine-learning-based natural language processing approaches. Int J Environ Res Public Health. 2020;17(24):9467. https:\/\/doi.org\/10.3390\/ijerph17249467. (PMID:33348764;PMCID:PMC7766498).","journal-title":"Int J Environ Res Public Health"},{"key":"1201_CR62","doi-asserted-by":"publisher","unstructured":"Zhang L, Xiong S, Zhu S, Tian J, Chen Q, Luo X, Guo H. Construction of Prediction Model of Foodborne Disease Outbreaks and Its Trend Prediction-Guizhou Province, China, 2023\u20132025. China CDC Wkly. 2024;6(18):408\u2013412. https:\/\/doi.org\/10.46234\/ccdcw2024.079. PMID: 38737480; PMCID: PMC11082649.","DOI":"10.46234\/ccdcw2024.079"},{"issue":"19","key":"1201_CR63","doi-asserted-by":"publisher","first-page":"6817","DOI":"10.3390\/ijerph20196817","volume":"20","author":"HJ Han","year":"2023","unstructured":"Han HJ, Suh HS. Predicting unmet healthcare needs in post-disaster: a machine learning approach. Int J Environ Res Public Health. 2023;20(19):6817. https:\/\/doi.org\/10.3390\/ijerph20196817. (PMID:37835087;PMCID:PMC10572666).","journal-title":"Int J Environ Res Public Health"},{"issue":"5","key":"1201_CR64","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1007\/s40618-023-02235-9","volume":"47","author":"F Giorgini","year":"2024","unstructured":"Giorgini F, Di Dalmazi G, Diciotti S. Artificial intelligence in endocrinology: a comprehensive review. J Endocrinol Invest. 2024;47(5):1067\u201382. https:\/\/doi.org\/10.1007\/s40618-023-02235-9. (Epub 2023 Nov 16. PMID: 37971630; PMCID: PMC11035463).","journal-title":"J Endocrinol Invest"},{"issue":"14","key":"1201_CR65","doi-asserted-by":"publisher","first-page":"4110","DOI":"10.3390\/jcm11144110","volume":"11","author":"M Sebastiani","year":"2022","unstructured":"Sebastiani M, Vacchi C, Manfredi A, Cassone G. Personalized medicine and machine learning: a roadmap for the future. J Clin Med. 2022;11(14):4110. https:\/\/doi.org\/10.3390\/jcm11144110.","journal-title":"J Clin Med"},{"issue":"13","key":"1201_CR66","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"2014","author":"K Kourou","year":"2015","unstructured":"Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;2014(13):8\u201317. https:\/\/doi.org\/10.1016\/j.csbj.2014.11.005. (eCollection 2015).","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"1201_CR67","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.ajhg.2018.11.002","volume":"104","author":"N Mavaddat","year":"2019","unstructured":"Mavaddat N, Michailidou K, Dennis J, et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet. 2019;104(1):21\u201334. https:\/\/doi.org\/10.1016\/j.ajhg.2018.11.002.","journal-title":"Am J Hum Genet"},{"issue":"13","key":"1201_CR68","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","volume":"375","author":"Z Obermeyer","year":"2016","unstructured":"Obermeyer Z, Emanuel EJ. Predicting the future\u2014big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216\u20139. https:\/\/doi.org\/10.1056\/NEJMp1606181.","journal-title":"N Engl J Med"},{"key":"1201_CR69","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1038\/nature22985","volume":"546","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa R, et al. Correction: corrigendum: dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;546:686. https:\/\/doi.org\/10.1038\/nature22985.","journal-title":"Nature"},{"key":"1201_CR70","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa R, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115\u20138. https:\/\/doi.org\/10.1038\/nature21056.","journal-title":"Nature"},{"issue":"1","key":"1201_CR71","doi-asserted-by":"publisher","first-page":"51","DOI":"10.4103\/abr.abr_383_21","volume":"12","author":"S Saeedbakhsh","year":"2023","unstructured":"Saeedbakhsh S, Sattari M, Mohammadi M, Najafian J, Mohammadi F. Diagnosis of coronary artery disease based on machine learning algorithms support vector machine artificial neural network, and random forest. Adv Biomed Res. 2023;12(1):51. https:\/\/doi.org\/10.4103\/abr.abr_383_21.","journal-title":"Adv Biomed Res"},{"key":"1201_CR72","doi-asserted-by":"publisher","unstructured":"Wang J, Chen Y. Federated Learning for Personalized Healthcare. In: Introduction to Transfer Learning. Machine Learning: Foundations, Methodologies, and Applications. Springer, Singapore. 2023. https:\/\/doi.org\/10.1007\/978-981-19-7584-4_19","DOI":"10.1007\/978-981-19-7584-4_19"},{"issue":"9","key":"1201_CR73","doi-asserted-by":"publisher","first-page":"4645","DOI":"10.3390\/ijms23094645","volume":"23","author":"M Hassan","year":"2022","unstructured":"Hassan M, Awan FM, Naz A, deAndr\u00e9s-Galiana EJ, Alvarez O, Cernea A, Fern\u00e1ndez-Brillet L, Fern\u00e1ndez-Mart\u00ednez JL, Kloczkowski A. Innovations in genomics and big data analytics for personalized medicine and health care: a review. Int J Mol Sci. 2022;23(9):4645. https:\/\/doi.org\/10.3390\/ijms23094645. (PMID:35563034;PMCID:PMC9104788).","journal-title":"Int J Mol Sci"},{"key":"1201_CR74","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1186\/s12864-022-08664-9","volume":"23","author":"Y Iwasaki","year":"2022","unstructured":"Iwasaki Y, Ikemura T, Wada K, et al. Comparative genomic analysis of the human genome and six bat genomes using unsupervised machine learning: Mb-level CpG and TFBS islands. BMC Genomics. 2022;23:497. https:\/\/doi.org\/10.1186\/s12864-022-08664-9.","journal-title":"BMC Genomics"},{"key":"1201_CR75","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s40246-022-00376-1","volume":"16","author":"B Jankovic","year":"2022","unstructured":"Jankovic B, Gojobori T. From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome. Hum Genomics. 2022;16:7. https:\/\/doi.org\/10.1186\/s40246-022-00376-1.","journal-title":"Hum Genomics"},{"key":"1201_CR76","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1186\/s13073-024-01345-0","volume":"16","author":"Y Gao","year":"2024","unstructured":"Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med. 2024;16:76. https:\/\/doi.org\/10.1186\/s13073-024-01345-0.","journal-title":"Genome Med"},{"key":"1201_CR77","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s13007-023-01073-3","volume":"19","author":"Q Yan","year":"2023","unstructured":"Yan Q, Fruzangohar M, Taylor J, et al. Improved genomic prediction using machine learning with Variational Bayesian sparsity. Plant Methods. 2023;19:96. https:\/\/doi.org\/10.1186\/s13007-023-01073-3.","journal-title":"Plant Methods"},{"key":"1201_CR78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-023-00352-y","volume":"17","author":"CW Chung","year":"2024","unstructured":"Chung CW, Chou SC, Hsiao TH, et al. Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records. BioData Mining. 2024;17:1. https:\/\/doi.org\/10.1186\/s13040-023-00352-y.","journal-title":"BioData Mining"},{"issue":"Suppl 6","key":"1201_CR79","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1186\/s12864-020-6661-6","volume":"21","author":"S Basodi","year":"2020","unstructured":"Basodi S, Baykal PI, Zelikovsky A, et al. Analysis of heterogeneous genomic samples using image normalization and machine learning. BMC Genomics. 2020;21(Suppl 6):405. https:\/\/doi.org\/10.1186\/s12864-020-6661-6.","journal-title":"BMC Genomics"},{"key":"1201_CR80","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1186\/s12888-020-02503-5","volume":"20","author":"S Sardaar","year":"2020","unstructured":"Sardaar S, Qi B, Dionne-Laporte A, et al. Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia. BMC Psychiatry. 2020;20:92. https:\/\/doi.org\/10.1186\/s12888-020-02503-5.","journal-title":"BMC Psychiatry"},{"key":"1201_CR81","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1186\/s12864-023-09933-x","volume":"25","author":"V Louren\u00e7o","year":"2024","unstructured":"Louren\u00e7o V, Ogutu J, Rodrigues R, et al. Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data. BMC Genomics. 2024;25:152. https:\/\/doi.org\/10.1186\/s12864-023-09933-x.","journal-title":"BMC Genomics"},{"key":"1201_CR82","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s13229-023-00549-2","volume":"14","author":"N Donnelly","year":"2023","unstructured":"Donnelly N, Cunningham A, Salas SM, et al. Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach. Molecular Autism. 2023;14:19. https:\/\/doi.org\/10.1186\/s13229-023-00549-2.","journal-title":"Molecular Autism"},{"key":"1201_CR83","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1186\/s12967-024-05090-1","volume":"22","author":"Z Alireza","year":"2024","unstructured":"Alireza Z, Maleeha M, Kaikkonen M, et al. Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection. J Transl Med. 2024;22:356. https:\/\/doi.org\/10.1186\/s12967-024-05090-1.","journal-title":"J Transl Med"},{"key":"1201_CR84","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1186\/s12967-023-04308-y","volume":"21","author":"M Dal Bo","year":"2023","unstructured":"Dal Bo M, Polano M, Ius T, et al. Machine learning to improve interpretability of clinical, radiological, and panel-based genomic data of glioma grade 4 patients undergoing surgical resection. J Transl Med. 2023;21:450. https:\/\/doi.org\/10.1186\/s12967-023-04308-y.","journal-title":"J Transl Med"},{"key":"1201_CR85","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1186\/s12967-023-04720-4","volume":"21","author":"V Zelli","year":"2023","unstructured":"Zelli V, Manno A, Compagnoni C, et al. Classification of tumor types using XGBoost machine learning model: a vector space transformation of genomic alterations. J Transl Med. 2023;21:836. https:\/\/doi.org\/10.1186\/s12967-023-04720-4.","journal-title":"J Transl Med"},{"key":"1201_CR86","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1186\/1471-2105-14-170","volume":"14","author":"SW Chang","year":"2013","unstructured":"Chang SW, Abdul-Kareem S, Merican AF, et al. Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods. BMC Bioinform. 2013;14:170. https:\/\/doi.org\/10.1186\/1471-2105-14-170.","journal-title":"BMC Bioinform"},{"key":"1201_CR87","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1186\/s12864-023-09667-w","volume":"24","author":"P Castelli","year":"2023","unstructured":"Castelli P, De Ruvo A, Bucciacchio A, et al. Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data. BMC Genomics. 2023;24:560. https:\/\/doi.org\/10.1186\/s12864-023-09667-w.","journal-title":"BMC Genomics"},{"key":"1201_CR88","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s13040-024-00355-3","volume":"17","author":"B Yald\u0131z","year":"2024","unstructured":"Yald\u0131z B, Erdo\u011fan O, Rafatov S, et al. Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies. BioData Mining. 2024;17:3. https:\/\/doi.org\/10.1186\/s13040-024-00355-3.","journal-title":"BioData Mining"},{"key":"1201_CR89","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1186\/s12859-019-3158-x","volume":"20","author":"J De Velasco Oriol","year":"2019","unstructured":"De Velasco Oriol J, Vallejo EE, Estrada K, et al. Benchmarking machine learning models for late-onset alzheimer\u2019s disease prediction from genomic data. BMC Bioinform. 2019;20:709. https:\/\/doi.org\/10.1186\/s12859-019-3158-x.","journal-title":"BMC Bioinform"},{"key":"1201_CR90","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1186\/s12859-024-05648-2","volume":"25","author":"AMA Elsherbini","year":"2024","unstructured":"Elsherbini AMA, Elkholy AH, Fadel YM, et al. Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through machine and deep learning techniques. BMC Bioinform. 2024;25:131. https:\/\/doi.org\/10.1186\/s12859-024-05648-2.","journal-title":"BMC Bioinform"},{"issue":"Suppl 1","key":"1201_CR91","doi-asserted-by":"publisher","first-page":"S13","DOI":"10.1186\/1471-2164-9-S1-S13","volume":"9","author":"M Pirooznia","year":"2008","unstructured":"Pirooznia M, Yang JY, Yang MQ, et al. A comparative study of different machine learning methods on microarray gene expression data. BMC Genomics. 2008;9(Suppl 1):S13. https:\/\/doi.org\/10.1186\/1471-2164-9-S1-S13.","journal-title":"BMC Genomics"},{"key":"1201_CR92","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1186\/s12916-018-1122-7","volume":"16","author":"H Fr\u00f6hlich","year":"2018","unstructured":"Fr\u00f6hlich H, Balling R, Beerenwinkel N, et al. From hype to reality: data science enabling personalized medicine. BMC Med. 2018;16:150. https:\/\/doi.org\/10.1186\/s12916-018-1122-7.","journal-title":"BMC Med"},{"key":"1201_CR93","doi-asserted-by":"publisher","first-page":"136988","DOI":"10.1109\/ACCESS.2023.3336424","volume":"11","author":"MHR Bhatti","year":"2023","unstructured":"Bhatti MHR, Javaid N, Mansoor B, Alrajeh N, Aslam M, Asad M. New hybrid deep learning models to predict cost from healthcare providers in smart hospitals. IEEE Access. 2023;11:136988\u20137010. https:\/\/doi.org\/10.1109\/ACCESS.2023.3336424.","journal-title":"IEEE Access"},{"issue":"2","key":"1201_CR94","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1109\/JSAC.2020.3021570","volume":"39","author":"L Sanabria-Russo","year":"2021","unstructured":"Sanabria-Russo L, Serra J, Pubill D, Verikoukis C. CURATE: on-demand orchestration of services for health emergencies prediction and mitigation. IEEE J Sel Areas Commun. 2021;39(2):438\u201345. https:\/\/doi.org\/10.1109\/JSAC.2020.3021570.","journal-title":"IEEE J Sel Areas Commun"},{"key":"1201_CR95","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1186\/s12916-020-01871-9","volume":"18","author":"KM Fiest","year":"2020","unstructured":"Fiest KM, Krewulak KD, Plotnikoff KM, et al. Allocation of intensive care resources during an infectious disease outbreak: a rapid review to inform practice. BMC Med. 2020;18:404. https:\/\/doi.org\/10.1186\/s12916-020-01871-9.","journal-title":"BMC Med"},{"key":"1201_CR96","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s12911-023-02159-7","volume":"23","author":"X Lu","year":"2023","unstructured":"Lu X, Qiu H. Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning. BMC Med Inform Decis Mak. 2023;23:59. https:\/\/doi.org\/10.1186\/s12911-023-02159-7.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1201_CR97","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1186\/s12911-024-02506-2","volume":"24","author":"V Torri","year":"2024","unstructured":"Torri V, Ercolanoni M, Bortolan F, et al. A NLP-based semi-automatic identification system for delays in follow-up examinations: an Italian case study on clinical referrals. BMC Med Inform Decis Mak. 2024;24:107. https:\/\/doi.org\/10.1186\/s12911-024-02506-2.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1201_CR98","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s41512-020-00079-y","volume":"4","author":"DM Kent","year":"2020","unstructured":"Kent DM, Paulus JK, Sharp RR, et al. When predictions are used to allocate scarce health care resources: three considerations for models in the era of COVID-19. Diagn Progn Res. 2020;4:11. https:\/\/doi.org\/10.1186\/s41512-020-00079-y.","journal-title":"Diagn Progn Res"},{"key":"1201_CR99","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/s42522-021-00052-9","volume":"3","author":"IR Mremi","year":"2021","unstructured":"Mremi IR, George J, Rumisha SF, et al. Twenty years of integrated disease surveillance and response in Sub-Saharan Africa: challenges and opportunities for effective management of infectious disease epidemics. One Health Outlook. 2021;3:22. https:\/\/doi.org\/10.1186\/s42522-021-00052-9.","journal-title":"One Health Outlook"},{"key":"1201_CR100","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1186\/s12911-022-01878-7","volume":"22","author":"J Tuominen","year":"2022","unstructured":"Tuominen J, Lomio F, Oksala N, et al. Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach. BMC Med Inform Decis Mak. 2022;22:134. https:\/\/doi.org\/10.1186\/s12911-022-01878-7.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1201_CR101","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s12911-018-0623-9","volume":"18","author":"R Jackson","year":"2018","unstructured":"Jackson R, Kartoglu I, Stringer C, et al. CogStack-experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital. BMC Med Inform Decis Mak. 2018;18:47. https:\/\/doi.org\/10.1186\/s12911-018-0623-9.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1201_CR102","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1186\/s12913-017-2407-9","volume":"17","author":"L Luo","year":"2017","unstructured":"Luo L, Luo L, Zhang X, et al. Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv Res. 2017;17:469. https:\/\/doi.org\/10.1186\/s12913-017-2407-9.","journal-title":"BMC Health Serv Res"},{"key":"1201_CR103","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1186\/s12888-023-04688-x","volume":"23","author":"M Kim","year":"2023","unstructured":"Kim M, Holton M, Sweeting A, et al. Using health administrative data to model associations and predict hospital admissions and length of stay for people with eating disorders. BMC Psychiatry. 2023;23:326. https:\/\/doi.org\/10.1186\/s12888-023-04688-x.","journal-title":"BMC Psychiatry"},{"key":"1201_CR104","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1186\/s12911-020-01281-0","volume":"20","author":"CH Cheng","year":"2020","unstructured":"Cheng CH, Kuo YH, Zhou Z. Outbreak minimization v.s. influence maximization: an optimization framework. BMC Med Inform Decis Mak. 2020;20:266. https:\/\/doi.org\/10.1186\/s12911-020-01281-0.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1201_CR105","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1186\/s12911-020-01256-1","volume":"20","author":"Y Huang","year":"2020","unstructured":"Huang Y, Xu C, Ji M, et al. Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method. BMC Med Inform Decis Mak. 2020;20:237. https:\/\/doi.org\/10.1186\/s12911-020-01256-1.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1201_CR106","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1186\/s13018-024-04576-4","volume":"19","author":"W Li","year":"2024","unstructured":"Li W, Zhang Y, Zhou X, et al. Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study. J Orthop Surg Res. 2024;19:112. https:\/\/doi.org\/10.1186\/s13018-024-04576-4.","journal-title":"J Orthop Surg Res"},{"key":"1201_CR107","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s13561-023-00456-5","volume":"13","author":"JC Reboredo","year":"2023","unstructured":"Reboredo JC, Barba-Queiruga JR, Ojea-Ferreiro J, et al. Forecasting emergency department arrivals using INGARCH models. Health Econ Rev. 2023;13:51. https:\/\/doi.org\/10.1186\/s13561-023-00456-5.","journal-title":"Health Econ Rev"},{"key":"1201_CR108","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1186\/s12913-021-06912-4","volume":"21","author":"NB Medeiros","year":"2021","unstructured":"Medeiros NB, Fogliatto FS, Rocha MK, et al. Forecasting the length-of-stay of pediatric patients in hospitals: a scoping review. BMC Health Serv Res. 2021;21:938. https:\/\/doi.org\/10.1186\/s12913-021-06912-4.","journal-title":"BMC Health Serv Res"},{"issue":"8","key":"1201_CR109","doi-asserted-by":"publisher","first-page":"1595","DOI":"10.1007\/s10964-023-01767-w","volume":"52","author":"WA Rothenberg","year":"2023","unstructured":"Rothenberg WA, Bizzego A, Esposito G, Lansford JE, Al-Hassan SM, Bacchini D, Bornstein MH, Chang L, Deater-Deckard K, Di Giunta L, Dodge KA, Gurdal S, Liu Q, Long Q, Oburu P, Pastorelli C, Skinner AT, Sorbring E, Tapanya S, Steinberg L, Tirado LMU, Yotanyamaneewong S, Alampay LP. Predicting adolescent mental health outcomes across cultures: a machine learning approach. J Youth Adolesc. 2023;52(8):1595\u2013619. https:\/\/doi.org\/10.1007\/s10964-023-01767-w. (Epub 2023 Apr 19. PMID: 37074622; PMCID: PMC10113992).","journal-title":"J Youth Adolesc"},{"key":"1201_CR110","doi-asserted-by":"publisher","DOI":"10.2196\/55747","volume":"11","author":"C Sweeney","year":"2024","unstructured":"Sweeney C, Ennis E, Mulvenna MD, Bond R, O\u2019Neill S. Insights derived from text-based digital media, in relation to mental health and suicide prevention, using data analysis and machine learning: systematic review. JMIR Ment Health. 2024;11: e55747. https:\/\/doi.org\/10.2196\/55747. (PMID:38935419;PMCID:PMC11240075).","journal-title":"JMIR Ment Health"},{"issue":"6","key":"1201_CR111","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1038\/s41591-022-01811-5","volume":"28","author":"R Garriga","year":"2022","unstructured":"Garriga R, Mas J, Abraha S, Nolan J, Harrison O, Tadros G, Matic A. Machine learning model to predict mental health crises from electronic health records. Nat Med. 2022;28(6):1240\u20138. https:\/\/doi.org\/10.1038\/s41591-022-01811-5. (Epub 2022 May 16. PMID: 35577964; PMCID: PMC9205775).","journal-title":"Nat Med"},{"key":"1201_CR112","doi-asserted-by":"publisher","DOI":"10.2196\/44322","volume":"11","author":"K Van Mens","year":"2023","unstructured":"Van Mens K, Lokkerbol J, Wijnen B, Janssen R, de Lange R, Tiemens B. Predicting undesired treatment outcomes with machine learning in mental health care: multisite study. JMIR Med Inform. 2023;11: e44322. https:\/\/doi.org\/10.2196\/44322. (PMID:37623374;PMCID:PMC10466445).","journal-title":"JMIR Med Inform"},{"issue":"1","key":"1201_CR113","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s41398-023-02536-w","volume":"13","author":"M Cao","year":"2023","unstructured":"Cao M, Martin E, Li X. Machine learning in attention-deficit\/hyperactivity disorder: new approaches toward understanding the neural mechanisms. Transl Psychiatry. 2023;13(1):236. https:\/\/doi.org\/10.1038\/s41398-023-02536-w. (PMID:37391419;PMCID:PMC10313824).","journal-title":"Transl Psychiatry"},{"issue":"6","key":"1201_CR114","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0304132","volume":"19","author":"MA Rahman","year":"2024","unstructured":"Rahman MA, Kohli T. Mental health analysis of international students using machine learning techniques. PLoS ONE. 2024;19(6): e0304132. https:\/\/doi.org\/10.1371\/journal.pone.0304132. (PMID:38843140;PMCID:PMC11156345).","journal-title":"PLoS ONE"},{"issue":"1","key":"1201_CR115","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1186\/s12916-023-02941-4","volume":"21","author":"Z Chen","year":"2023","unstructured":"Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med. 2023;21(1):241. https:\/\/doi.org\/10.1186\/s12916-023-02941-4. (PMID:37400814;PMCID:PMC10318841).","journal-title":"BMC Med"},{"issue":"Suppl 1","key":"1201_CR116","doi-asserted-by":"publisher","first-page":"S48","DOI":"10.1093\/infdis\/jiac293","volume":"227","author":"SS Mukerji","year":"2023","unstructured":"Mukerji SS, Petersen KJ, Pohl KM, Dastgheyb RM, Fox HS, Bilder RM, Brouillette MJ, Gross AL, Scott-Sheldon LAJ, Paul RH, Gabuzda D. Machine learning approaches to understand cognitive phenotypes in people with HIV. J Infect Dis. 2023;227(Suppl 1):S48\u201357. https:\/\/doi.org\/10.1093\/infdis\/jiac293. (PMID:36930638;PMCID:PMC10022709).","journal-title":"J Infect Dis"},{"issue":"5","key":"1201_CR117","doi-asserted-by":"publisher","DOI":"10.2196\/15708","volume":"23","author":"A Le Glaz","year":"2021","unstructured":"Le Glaz A, Haralambous Y, Kim-Dufor DH, Lenca P, Billot R, Ryan TC, Marsh J, DeVylder J, Walter M, Berrouiguet S, Lemey C. Machine learning and natural language processing in mental health: systematic review. J Med Internet Res. 2021;23(5): e15708. https:\/\/doi.org\/10.2196\/15708. (PMID:33944788;PMCID:PMC8132982).","journal-title":"J Med Internet Res"},{"issue":"1","key":"1201_CR118","doi-asserted-by":"publisher","first-page":"17548","DOI":"10.1038\/s41598-021-96801-x","volume":"11","author":"S Mukherjee","year":"2021","unstructured":"Mukherjee S, Frimpong Boamah E, Ganguly P, Botchwey N. A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus. Sci Rep. 2021;11(1):17548. https:\/\/doi.org\/10.1038\/s41598-021-96801-x. (PMID:34475452;PMCID:PMC8413383).","journal-title":"Sci Rep"},{"issue":"4","key":"1201_CR119","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1111\/pcn.13322","volume":"76","author":"K Shiba","year":"2022","unstructured":"Shiba K, Daoud A, Kino S, Nishi D, Kondo K, Kawachi I. Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: a machine learning approach. Psychiatry Clin Neurosci. 2022;76(4):97\u2013105. https:\/\/doi.org\/10.1111\/pcn.13322. (Epub 2022 Jan 21. PMID: 34936171; PMCID: PMC9102396).","journal-title":"Psychiatry Clin Neurosci"},{"key":"1201_CR120","doi-asserted-by":"publisher","DOI":"10.2196\/42734","volume":"25","author":"NH Di Cara","year":"2023","unstructured":"Di Cara NH, Maggio V, Davis OSP, Haworth CMA. Methodologies for monitoring mental health on twitter: systematic review. J Med Internet Res. 2023;25: e42734. https:\/\/doi.org\/10.2196\/42734. (PMID:37155236;PMCID:PMC10203928).","journal-title":"J Med Internet Res"},{"issue":"3","key":"1201_CR121","doi-asserted-by":"publisher","first-page":"2636","DOI":"10.3390\/ijerph20032636","volume":"20","author":"T Robinson","year":"2023","unstructured":"Robinson T, Condell J, Ramsey E, Leavey G. Self-management of subclinical common mental health disorders (Anxiety, Depression and Sleep Disorders) using wearable devices. Int J Environ Res Public Health. 2023;20(3):2636. https:\/\/doi.org\/10.3390\/ijerph20032636. (PMID:36768002;PMCID:PMC9916237).","journal-title":"Int J Environ Res Public Health"},{"key":"1201_CR122","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2023.947081","volume":"14","author":"L Yao","year":"2023","unstructured":"Yao L, Wang Z, Gu H, Zhao X, Chen Y, Liu L. Prediction of Chinese clients\u2019 satisfaction with psychotherapy by machine learning. Front Psychiatry. 2023;14: 947081. https:\/\/doi.org\/10.3389\/fpsyt.2023.947081. (PMID:36741124;PMCID:PMC9893506).","journal-title":"Front Psychiatry"},{"issue":"10","key":"1201_CR123","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e31158","volume":"10","author":"AE Khan","year":"2024","unstructured":"Khan AE, Hasan MJ, Anjum H, Mohammed N, Momen S. Predicting life satisfaction using machine learning and explainable AI. Heliyon. 2024;10(10): e31158. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e31158. (PMID:38818204;PMCID:PMC11137391).","journal-title":"Heliyon"},{"issue":"12","key":"1201_CR124","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.17133","volume":"29","author":"A Sundaram","year":"2024","unstructured":"Sundaram A, Subramaniam H, Ab Hamid SH, Mohamad NA. An adaptive data-driven architecture for mental health care applications. PeerJ. 2024;29(12): e17133. https:\/\/doi.org\/10.7717\/peerj.17133. (PMID:38563009;PMCID:PMC10984189).","journal-title":"PeerJ"},{"issue":"6","key":"1201_CR125","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1038\/s41551-023-01056-8","volume":"7","author":"RJ Chen","year":"2023","unstructured":"Chen RJ, Wang JJ, Williamson DFK, Chen TY, Lipkova J, Lu MY, Sahai S, Mahmood F. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng. 2023;7(6):719\u201342. https:\/\/doi.org\/10.1038\/s41551-023-01056-8. (Epub 2023 Jun 28. PMID: 37380750; PMCID: PMC10632090).","journal-title":"Nat Biomed Eng."},{"issue":"1","key":"1201_CR126","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/s12911-024-02417-2","volume":"24","author":"CH Lai","year":"2024","unstructured":"Lai CH, Mok PK, Chau WW, Law SW. Application of machine learning models on predicting the length of hospital stay in fragility fracture patients. BMC Med Inform Decis Mak. 2024;24(1):26. https:\/\/doi.org\/10.1186\/s12911-024-02417-2. (PMID:38291406;PMCID:PMC10826155).","journal-title":"BMC Med Inform Decis Mak"},{"key":"1201_CR127","doi-asserted-by":"publisher","unstructured":"Robert SA, Liu AY. Changes in public awareness of the social determinants of health over 15 years in Wisconsin, United States, Preventive Medicine Reports. Prev Med Reports 2025;50:102965, ISSN 2211\u20133355, https:\/\/doi.org\/10.1016\/j.pmedr.2025.102965.","DOI":"10.1016\/j.pmedr.2025.102965"},{"key":"1201_CR128","doi-asserted-by":"publisher","unstructured":"Herd D. Policing as a social determinant of health in three decades of public health research: a systematic review. SSM Popul Health, https:\/\/doi.org\/10.1016\/j.ssmph.2025.101801.","DOI":"10.1016\/j.ssmph.2025.101801"},{"key":"1201_CR129","doi-asserted-by":"publisher","DOI":"10.1016\/j.amepre.2025.04.010","author":"S Lim","year":"2025","unstructured":"Lim S, Bekemeier B, Pintye J, Grembowski D. The association between social determinants of health and case rates of sexually transmitted infections at the county-level in the US from 2000\u20132019. Am J Prev Med. 2025. https:\/\/doi.org\/10.1016\/j.amepre.2025.04.010.","journal-title":"Am J Prev Med"},{"key":"1201_CR130","doi-asserted-by":"publisher","unstructured":"Oncel D, Ravi R, Arolli X, Hoyek S, Chaaya C, Berrocal AM, Patel NA. A comparison of pediatric and adult ocular diseases in the context of social determinants of health. AJO Inter. 2025:100128, ISSN 2950\u20132535, https:\/\/doi.org\/10.1016\/j.ajoint.2025.100128.","DOI":"10.1016\/j.ajoint.2025.100128"},{"issue":"4","key":"1201_CR131","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1093\/idpl\/ipab020","volume":"11","author":"R Binns","year":"2021","unstructured":"Binns R, Veale M. Is that your final decision? Multi-stage profiling, selective effects, and Article 22 of the GDPR. Int Data Privacy Law. 2021;11(4):319\u201332. https:\/\/doi.org\/10.1093\/idpl\/ipab020.","journal-title":"Int Data Privacy Law"},{"key":"1201_CR132","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"A Johnson","year":"2016","unstructured":"Johnson A, Pollard T, Shen L, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3: 160035. https:\/\/doi.org\/10.1038\/sdata.2016.35.","journal-title":"Sci Data"},{"key":"1201_CR133","doi-asserted-by":"publisher","unstructured":"Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A Survey on Bias and Fairness in Machine Learning. ACM Comput. Surv. 2021;54(6), Article 115 (July 2022), 35. https:\/\/doi.org\/10.1145\/3457607","DOI":"10.1145\/3457607"},{"key":"1201_CR134","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1:206\u201315. https:\/\/doi.org\/10.1038\/s42256-019-0048-x.","journal-title":"Nat Mach Intell"},{"issue":"23","key":"1201_CR135","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1001\/jama.2019.4914","volume":"321","author":"EJ Emanuel","year":"2019","unstructured":"Emanuel EJ, Wachter RM. Artificial intelligence in health care: Will the Value Match the Hype? JAMA. 2019;321(23):2281\u20132. https:\/\/doi.org\/10.1001\/jama.2019.4914.","journal-title":"JAMA"},{"key":"1201_CR136","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25:24\u20139. https:\/\/doi.org\/10.1038\/s41591-018-0316-z.","journal-title":"Nat Med"},{"issue":"2","key":"1201_CR137","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003904","volume":"11","author":"E Vayena","year":"2015","unstructured":"Vayena E, Salath\u00e9 M, Madoff LC, Brownstein JS. Ethical challenges of big data in public health. PLoS Comput Biol. 2015;11(2): e1003904. https:\/\/doi.org\/10.1371\/journal.pcbi.1003904.","journal-title":"PLoS Comput Biol"},{"key":"1201_CR138","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44\u201356. https:\/\/doi.org\/10.1038\/s41591-018-0300-7.","journal-title":"Nat Med"},{"key":"1201_CR139","doi-asserted-by":"publisher","unstructured":"Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vascular Neurol 2017;2(4), 230\u2013243: e000101. https:\/\/doi.org\/10.1136\/svn-2017-000101.","DOI":"10.1136\/svn-2017-000101"},{"key":"1201_CR140","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.3390\/healthcare10122493W","volume":"10","author":"NN Khanna","year":"2022","unstructured":"Khanna NN, Maindarkar MA, Viswanathan V, Fernandes JFE, Paul S, Bhagawati M, Ahluwalia P, Ruzsa Z, Sharma A, Kolluri R, et al. Economics of artificial intelligence in healthcare: diagnosis vs. treatment. Healthcare. 2022;10:2493. https:\/\/doi.org\/10.3390\/healthcare10122493W.","journal-title":"Healthcare"},{"key":"1201_CR141","doi-asserted-by":"publisher","first-page":"123445","DOI":"10.1109\/ACCESS.2023.3327905","volume":"11","author":"W Jiao","year":"2023","unstructured":"Jiao W, Zhang X, D\u2019Souza F. The economic value and clinical impact of artificial intelligence in healthcare: a scoping literature review. IEEE Access. 2023;11:123445\u201357. https:\/\/doi.org\/10.1109\/ACCESS.2023.3327905.","journal-title":"IEEE Access"},{"issue":"2021","key":"1201_CR142","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1146\/annurev-biodatasci-092820-114757","volume":"4","author":"IY Chen","year":"2021","unstructured":"Chen IY, et al. Ethical machine learning in health care: a critical review. Ann Rev Biomed Data Sci. 2021;4(2021):123\u201344. https:\/\/doi.org\/10.1146\/annurev-biodatasci-092820-114757.","journal-title":"Ann Rev Biomed Data Sci"},{"key":"1201_CR143","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1146\/annurev-biodatasci-092820-114757","volume":"4","author":"IY Chen","year":"2021","unstructured":"Chen IY, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M. Ethical machine learning in healthcare. Ann Rev Biomed Data Sci. 2021;4:123\u201344. https:\/\/doi.org\/10.1146\/annurev-biodatasci-092820-114757.","journal-title":"Ann Rev Biomed Data Sci"},{"issue":"1","key":"1201_CR144","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1093\/cid\/cix731","volume":"66","author":"J Wiens","year":"2018","unstructured":"Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018;66(1):149\u201353. https:\/\/doi.org\/10.1093\/cid\/cix731.","journal-title":"Clin Infect Dis"},{"issue":"1","key":"1201_CR145","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2021-100444","volume":"28","author":"S Reddy","year":"2021","unstructured":"Reddy S, et al. Evaluation of artificial intelligence in healthcare: analysis of the metrics and benchmarking process. BMJ Health Care Inform. 2021;28(1): e100444. https:\/\/doi.org\/10.1136\/bmjhci-2021-100444.","journal-title":"BMJ Health Care Inform"},{"issue":"2","key":"1201_CR146","first-page":"207","volume":"9","author":"G Martin","year":"2020","unstructured":"Martin G, et al. AI in healthcare: industry initiatives and a place for regulation? Health Policy Technol. 2020;9(2):207\u201318.","journal-title":"Health Policy Technol"},{"key":"1201_CR147","doi-asserted-by":"publisher","first-page":"1368377","DOI":"10.3389\/fmicb.2024.1368377","volume":"15","author":"E Lange","year":"2024","unstructured":"Lange E, Kranert L, Kr\u00fcger J, Benndorf D, Heyer R. Microbiome modeling: a beginner\u2019s guide. Front Microbiol. 2024;15:1368377. https:\/\/doi.org\/10.3389\/fmicb.2024.1368377.","journal-title":"Front Microbiol"},{"issue":"8","key":"1201_CR148","doi-asserted-by":"publisher","DOI":"10.2196\/20285","volume":"22","author":"D Liu","year":"2020","unstructured":"Liu D, Clemente L, Poirier C, Ding X, Chinazzi M, Davis J, Vespignani A, Santillana M. Real-time forecasting of the COVID-19 outbreak in chinese provinces: machine learning approach using novel digital data and estimates from mechanistic models. J Med Internet Res. 2020;22(8): e20285. https:\/\/doi.org\/10.2196\/20285. (PMID:32730217PMCID:7459435).","journal-title":"J Med Internet Res"},{"key":"1201_CR149","doi-asserted-by":"publisher","unstructured":"Tonekaboni S et al. What clinicians want: contextualizing explainable machine learning for clinical end use. Proc Mach Learn Res 2019;106:1\u201321 https:\/\/doi.org\/10.48550\/arXiv.1905.05134.","DOI":"10.48550\/arXiv.1905.05134"},{"issue":"3","key":"1201_CR150","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Talwalkar A, Smith V. Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag. 2020;37(3):50\u201360. https:\/\/doi.org\/10.1109\/MSP.2020.2975749.","journal-title":"IEEE Signal Process Mag"},{"issue":"5","key":"1201_CR151","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1002\/humu.22080","volume":"33","author":"PN Robinson","year":"2012","unstructured":"Robinson PN. Deep phenotyping for precision medicine. Hum Mutat. 2012;33(5):777\u201380. https:\/\/doi.org\/10.1002\/humu.22080. (PMID: 22504886).","journal-title":"Hum Mutat"},{"issue":"6","key":"1201_CR152","doi-asserted-by":"publisher","DOI":"10.1242\/dmm.049376","volume":"15","author":"AK Schalkamp","year":"2022","unstructured":"Schalkamp AK, Rahman N, Monz\u00f3n-Sandoval J, Sandor C. Deep phenotyping for precision medicine in Parkinson\u2019s disease. Dis Model Mech. 2022;15(6): 049376. https:\/\/doi.org\/10.1242\/dmm.049376. (Epub 2022 Jun 1. PMID: 35647913; PMCID: PMC9178512).","journal-title":"Dis Model Mech"},{"key":"1201_CR153","doi-asserted-by":"publisher","unstructured":"Lees JA, Russell TW, Shaw LP, Hellewell J. Recent approaches in computational modelling for controlling pathogen threats. Life Sci Alliance. 2024;7(9):e202402666. https:\/\/doi.org\/10.26508\/lsa.202402666. PMID: 38906676; PMCID: PMC11192964.","DOI":"10.26508\/lsa.202402666"},{"key":"1201_CR154","doi-asserted-by":"publisher","unstructured":"Menzies NA, Wolf E, Connors D, Bellerose M, Sbarra AN, Cohen T, Hill AN, Yaesoubi R, Galer K, White PJ, Abubakar I, Salomon JA. Progression from latent infection to active disease in dynamic tuberculosis transmission models: a systematic review of the validity of modelling assumptions. Lancet Infect Dis. 2018;18(8):e228-e238. https:\/\/doi.org\/10.1016\/S1473-3099(18)30134-8. Epub 2018 Apr 10. Erratum in: Lancet Infect Dis. 2018 Nov;18(11):1177. https:\/\/doi.org\/10.1016\/S1473-3099(18)30603-0. PMID: 29653698; PMCID: PMC6070419.","DOI":"10.1016\/S1473-3099(18)30134-8"},{"key":"1201_CR155","doi-asserted-by":"publisher","unstructured":"Zhou S, Zhao J, and Zhang L (2022) Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview. Front. Psychiatry 13:811665. https:\/\/doi.org\/10.3389\/fpsyt.2022.811665Bibault, J. E., et al. (2019). AI and big data in cancer: revolutionizing patient care. Nature Reviews Clinical Oncology, 16(11), 663\u2013674.","DOI":"10.3389\/fpsyt.2022.811665Bibault"},{"key":"1201_CR156","doi-asserted-by":"publisher","first-page":"2300","DOI":"10.1016\/j.csbj.2020.08.019","volume":"18","author":"Z Dlamini","year":"2020","unstructured":"Dlamini Z, Francies FZ, Hull R, Marima R. Artificial intelligence (AI) and big data in cancer and precision oncology. Comput Struct Biotechnol J. 2020;18:2300\u201311. https:\/\/doi.org\/10.1016\/j.csbj.2020.08.019. (PMID:32994889;PMCID:PMC7490765).","journal-title":"Comput Struct Biotechnol J"},{"key":"1201_CR157","doi-asserted-by":"publisher","first-page":"1110088","DOI":"10.3389\/fpubh.2023.1110088","volume":"11","author":"J Andrew","year":"2023","unstructured":"Andrew J, Rudra M, Eunice J, Belfin RV. Artificial intelligence in adolescents\u2019 mental health disorder diagnosis, prognosis, and treatment. Front Public Health. 2023;11:1110088. https:\/\/doi.org\/10.3389\/fpubh.2023.1110088. (PMID:37064712;PMCID:PMC10102508).","journal-title":"Front Public Health"},{"key":"1201_CR158","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-818438-7.00012-5","author":"S Gerke","year":"2020","unstructured":"Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artif Intell Healthcare. 2020. https:\/\/doi.org\/10.1016\/B978-0-12-818438-7.00012-5. (Epub 2020 Jun 26. PMCID: PMC7332220).","journal-title":"Artif Intell Healthcare."},{"issue":"4","key":"1201_CR159","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgph.0001844","volume":"3","author":"A Farlow","year":"2023","unstructured":"Farlow A, Hoffmann A, Tadesse GA, Mzurikwao D, Beyer R, Akogo D, Weicken E, Matika T, Nweje MI, Wamae W, Arts S, Wiegand T, Bennett C, Farhat MR, Gr\u00f6schel MI. Rethinking global digital health and AI-for-health innovation challenges. PLOS Glob Public Health. 2023;3(4): e0001844. https:\/\/doi.org\/10.1371\/journal.pgph.0001844. (PMID:37115743;PMCID:PMC10146484).","journal-title":"PLOS Glob Public Health"},{"issue":"12","key":"1201_CR160","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.1038\/s41551-020-00640-6","volume":"4","author":"T Mishra","year":"2020","unstructured":"Mishra T, Wang M, Metwally AA, Bogu GK, Brooks AW, Bahmani A, Alavi A, Celli A, Higgs E, Dagan-Rosenfeld O, Fay B, Kirkpatrick S, Kellogg R, Gibson M, Wang T, Hunting EM, Mamic P, Ganz AB, Rolnik B, Li X, Snyder MP. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng. 2020;4(12):1208\u201320. https:\/\/doi.org\/10.1038\/s41551-020-00640-6. (Epub 2020 Nov 18. PMID: 33208926; PMCID: PMC9020268).","journal-title":"Nat Biomed Eng."},{"issue":"1","key":"1201_CR161","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1038\/s41746-021-00533-1","volume":"4","author":"M Gadaleta","year":"2021","unstructured":"Gadaleta M, Radin JM, Baca-Motes K, Ramos E, Kheterpal V, Topol EJ, Steinhubl SR, Quer G. Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms. NPJ Digit Med. 2021;4(1):166. https:\/\/doi.org\/10.1038\/s41746-021-00533-1. (PMID:34880366;PMCID:PMC8655005).","journal-title":"NPJ Digit Med"},{"issue":"6","key":"1201_CR162","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1093\/bib\/bbx044","volume":"19","author":"R Miotto","year":"2018","unstructured":"Miotto R, et al. Deep learning for healthcare: review, opportunities, and challenges. Brief Bioinform. 2018;19(6):1236\u201346. https:\/\/doi.org\/10.1093\/bib\/bbx044.","journal-title":"Brief Bioinform"},{"issue":"14","key":"1201_CR163","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1001\/jama.2020.3151","volume":"323","author":"CJ Wang","year":"2020","unstructured":"Wang CJ, Ng CY, Brook RH. Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA. 2020;323(14):1341\u20132. https:\/\/doi.org\/10.1001\/jama.2020.3151. (PMID: 32125371).","journal-title":"JAMA"},{"key":"1201_CR164","doi-asserted-by":"publisher","unstructured":"Kumar R, Singh A, Kassar AS, Humaida MI, Joshi S, Sharma M. Leveraging artificial intelligence to achieve sustainable public healthcare services in Saudi Arabia: a systematic literature review of critical success factors. CMES Comput Mod Eng Sci 2025;142(2): 1289\u20131349, ISSN 1526\u20131492, https:\/\/doi.org\/10.32604\/cmes.2025.059152.","DOI":"10.32604\/cmes.2025.059152"},{"key":"1201_CR165","doi-asserted-by":"publisher","unstructured":"Kalhori SR, Najafi F, Hasannejadasl H, Heydari S. Artificial intelligence-enabled obesity prediction: A systematic review of cohort data analysis. Int J Med Inform. 2025;196: 105804, ISSN 1386\u20135056, https:\/\/doi.org\/10.1016\/j.ijmedinf.2025.105804.","DOI":"10.1016\/j.ijmedinf.2025.105804"},{"key":"1201_CR166","doi-asserted-by":"publisher","DOI":"10.1016\/j.modpat.2025.1007052025\/04\/30","author":"MG Hanna","year":"2025","unstructured":"Hanna MG, Pantanowitz L, Dash R, Harrison JH, Deebajah M, Pantanowitz J, Rashidi HH. Future of artificial intelligence-machine learning trends in pathology and medicine. Modern Pathol. 2025. https:\/\/doi.org\/10.1016\/j.modpat.2025.1007052025\/04\/30.","journal-title":"Modern Pathol"},{"key":"1201_CR167","doi-asserted-by":"publisher","unstructured":"Fiske A, Blacker S, Genevi\u00e8ve LD, Willem T, Fritzsche MC, Buyx A, Celi LA, McLennan S. Weighing the benefits and risks of collecting race and ethnicity data in clinical settings for medical artificial intelligence. Lancet Digital Health, 2025;7(4):e286- e294. ISSN- 2589\u20137500, https:\/\/doi.org\/10.1016\/j.landig.2025.01.003","DOI":"10.1016\/j.landig.2025.01.003"},{"key":"1201_CR168","doi-asserted-by":"publisher","DOI":"10.1016\/j.jscai.2025.102567","author":"DB Holt","year":"2025","unstructured":"Holt DB, El-Bokl A, Stromberg D, Taylor MD. Role of artificial intelligence in congenital heart disease and interventions. J Soc Cardiovasc Angiogr Interv. 2025. https:\/\/doi.org\/10.1016\/j.jscai.2025.102567.","journal-title":"J Soc Cardiovasc Angiogr Interv"},{"issue":"2","key":"1201_CR169","doi-asserted-by":"publisher","DOI":"10.1016\/j.rh.2025.100911","volume":"59","author":"A Alshami","year":"2025","unstructured":"Alshami A, Nashwanb A, AlDardour A, Qusini A. Artificial Intelligence in rehabilitation: a narrative review on advancing patient care. Rehabilitaci\u00f3n. 2025;59(2): 100911. https:\/\/doi.org\/10.1016\/j.rh.2025.100911.","journal-title":"Rehabilitaci\u00f3n"},{"issue":"3","key":"1201_CR170","doi-asserted-by":"publisher","DOI":"10.1016\/j.modpat.2024.100686","volume":"38","author":"MG Hannaa","year":"2025","unstructured":"Hannaa MG, Pantanowitza L, Jacksonc B, Palmera O, Visweswaran S, Pantanowitz J, Deebajah M, Rashidi HH. Ethical and bias considerations in artificial intelligence\/machine learning. Mod Pathol. 2025;38(3): 100686. https:\/\/doi.org\/10.1016\/j.modpat.2024.100686.","journal-title":"Mod Pathol"},{"key":"1201_CR171","doi-asserted-by":"publisher","unstructured":"Almaiah MA, Bin Sulaiman R, Islam U, Badr Y, El-Qirem FA. Federated learning in healthcare: a bibliometric analysis of privacy, security, and adversarial threats (2021\u20132024). SHIFRA. 2025:46\u201361. https:\/\/doi.org\/10.70470\/SHIFRA\/2025\/002","DOI":"10.70470\/SHIFRA\/2025\/002"},{"key":"1201_CR172","doi-asserted-by":"publisher","first-page":"180815","DOI":"10.1109\/ACCESS.2024.3509353","volume":"12","author":"S Raza","year":"2024","unstructured":"Raza S, Shaban-Nejad A, Dolatabadi E, Mamiya H. Exploring bias and prediction metrics to characterise the fairness of machine learning for equity-centered public health decision-making: a narrative review. IEEE Access. 2024;12:180815\u201329. https:\/\/doi.org\/10.1109\/ACCESS.2024.3509353.","journal-title":"IEEE Access"},{"key":"1201_CR173","doi-asserted-by":"publisher","unstructured":"Mensah GB, Mijwil MM, Abotaleb M, Ali G, Dutta PK, Mzili T, Eid MM. Explainable AI for healthcare: training healthcare workers to use artificial intelligence techniques to reduce medical negligence in ghana\u2019s public health act, 2012 (Act 851). EDRAAK. 2025;1\u20136. https:\/\/doi.org\/10.70470\/EDRAAK\/2025\/001","DOI":"10.70470\/EDRAAK\/2025\/001"},{"key":"1201_CR174","doi-asserted-by":"crossref","unstructured":"Libin PJ, Moonens A, Verstraeten T, Perez-Sanjines F, Hens N, Lemey P, Now\u00e9 A. Deep reinforcement learning for large-scale epidemic control. In: Machine learning and knowledge discovery in databases. Applied data science and demo track: European conference, ECML PKDD 2020, Ghent, Belgium, September 14\u201318, 2020, Proceedings, Part V 2021 (pp. 155\u201370). Springer International Publishing.","DOI":"10.1007\/978-3-030-67670-4_10"},{"key":"1201_CR175","unstructured":"Fascia M. Machine learning applications in medical prognostics: a comprehensive review. arXiv preprint arXiv:2408.02344."}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01201-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01201-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01201-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T08:15:51Z","timestamp":1751616951000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01201-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,4]]},"references-count":175,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1201"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01201-x","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,4]]},"assertion":[{"value":"23 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"154"}}