{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T04:01:16Z","timestamp":1776830476861,"version":"3.51.2"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Netw Model Anal Health Inform Bioinforma"],"DOI":"10.1007\/s13721-025-00594-2","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T08:08:54Z","timestamp":1757318934000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Asthma exacerbation prediction using shallow and deep learning approaches: A systematic review"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9509-1915","authenticated-orcid":false,"given":"Stanley Ebhohimhen","family":"Abhadiomhen","sequence":"first","affiliation":[]},{"given":"Royransom Chiemela","family":"Nzeh","sequence":"additional","affiliation":[]},{"given":"Modesta Ero","family":"Ezema","sequence":"additional","affiliation":[]},{"given":"Abel Onolunosen","family":"Abhadionmhen","sequence":"additional","affiliation":[]},{"given":"Blessing Chimezie","family":"Uzo","sequence":"additional","affiliation":[]},{"given":"Assumpta Obianuju","family":"Ezugwu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"issue":"22","key":"594_CR1","first-page":"4405","volume":"13","author":"SE Abhadiomhen","year":"2024","unstructured":"Abhadiomhen SE, Nzeakor EO, Oyibo K (2024) Health Risk Assess Using Mach Learning: Syst Rev Electronics 13(22):4405","journal-title":"Health Risk Assess Using Mach Learning: Syst Rev Electronics"},{"key":"594_CR2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3440502","author":"DA Aliyu","year":"2024","unstructured":"Aliyu DA, Akhir EAP, Saidu Y, Adamu S, Umar KI, Bunu AS, Mamman H (2024) Optimization techniques for asthma exacerbation prediction models: a systematic literature review. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3440502","journal-title":"IEEE Access"},{"issue":"3","key":"594_CR3","doi-asserted-by":"publisher","first-page":"92","DOI":"10.51594\/imsrj.v3i3.641","volume":"3","author":"AO Babarinde","year":"2023","unstructured":"Babarinde AO, Ayo-Farai O, Maduka CP, Okongwu CC, Ogundairo O, Sodamade O (2023) Review of AI applications in healthcare: comparative insights from the USA and Africa. Int Med Sci Res J 3(3):92\u2013107","journal-title":"Int Med Sci Res J"},{"key":"594_CR4","volume":"3","author":"A Bhutoria","year":"2022","unstructured":"Bhutoria A (2022) Personalized education and artificial intelligence in the United States, China, and India: a systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence 3:100068","journal-title":"Computers and Education: Artificial Intelligence"},{"key":"594_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-020-0913-7","volume":"20","author":"J Bridge","year":"2020","unstructured":"Bridge J, Blakey JD, Bonnett LJ (2020) A systematic review of methodology used in the development of prediction models for future asthma exacerbation. BMC Med Res Methodol 20:1\u201312","journal-title":"BMC Med Res Methodol"},{"key":"594_CR6","doi-asserted-by":"publisher","DOI":"10.2196\/46717","volume":"2","author":"A Budiarto","year":"2023","unstructured":"Budiarto A, Tsang KC, Wilson AM, Sheikh A, Shah SA (2023) Machine learning\u2013based asthma attack prediction models from routinely collected electronic health records: systematic scoping review. JMIR AI 2:e46717","journal-title":"JMIR AI"},{"issue":"3","key":"594_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3582698","volume":"14","author":"M Cai","year":"2023","unstructured":"Cai M, Shen X, Abhadiomhen SE, Cai Y, Tian S (2023) Robust dimensionality reduction via low-rank laplacian graph learning. ACM Trans Intell Syst Technol 14(3):1\u201324","journal-title":"ACM Trans Intell Syst Technol"},{"key":"594_CR8","doi-asserted-by":"publisher","DOI":"10.2147\/JAA.S471964","author":"JY Choi","year":"2024","unstructured":"Choi JY, Rhee CK (2024) Predicting asthma exacerbation risk in the adult South Korean population using integrated health data and machine learning models. J Asthma Allergy. https:\/\/doi.org\/10.2147\/JAA.S471964","journal-title":"J Asthma Allergy"},{"key":"594_CR9","doi-asserted-by":"crossref","unstructured":"Chung KF (2018) Diagnosis and management of severe asthma. Seminars in respiratory and critical care medicine, vol 39. Thieme Medical, pp 091\u2013099. 01","DOI":"10.1055\/s-0037-1607391"},{"key":"594_CR10","unstructured":"Dahane A, Benameur R, Abainia K, Benatta D, Meneceur S, Brakna C, Mellouk A (2023). An Innovative Low-cost IoT-Based Asthma Exacerbation Prediction System Using Federated Learning. In IAM (pp. 12\u201320)."},{"issue":"1","key":"594_CR11","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s10916-024-02061-3","volume":"48","author":"WK Darsha Jayamini","year":"2024","unstructured":"Darsha Jayamini WK, Mirza F, Asif Naeem M, Chan AHY (2024) Investigating machine learning techniques for predicting risk of asthma exacerbations: a systematic review. J Med Syst 48(1):49","journal-title":"J Med Syst"},{"issue":"1","key":"594_CR12","doi-asserted-by":"publisher","first-page":"20363","DOI":"10.1038\/s41598-022-24909-9","volume":"12","author":"AA de Hond","year":"2022","unstructured":"de Hond AA, Kant IM, Honkoop PJ, Smith AD, Steyerberg EW, Sont JK (2022) Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations. Sci Rep 12(1):20363","journal-title":"Sci Rep"},{"key":"594_CR13","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1016\/j.matpr.2021.04.326","volume":"81","author":"S Devulapalli","year":"2023","unstructured":"Devulapalli S, Potti A, Krishnan R, Khan MS (2023) Experimental evaluation of unsupervised image retrieval application using hybrid feature extraction by integrating deep learning and handcrafted techniques. Mater Today Proc 81:983\u2013988","journal-title":"Mater Today Proc"},{"issue":"10","key":"594_CR14","doi-asserted-by":"publisher","first-page":"2526","DOI":"10.3390\/diagnostics12102526","volume":"12","author":"SK Dhillon","year":"2022","unstructured":"Dhillon SK, Ganggayah MD, Sinnadurai S, Lio P, Taib NA (2022) Theory and practice of integrating machine learning and conventional statistics in medical data analysis. Diagnostics 12(10):2526","journal-title":"Diagnostics"},{"key":"594_CR15","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.procs.2017.08.343","volume":"113","author":"Q Do","year":"2017","unstructured":"Do Q, Son TC, Chaudri J (2017) Classification of asthma severity and medication using tensorflow and multilevel databases. Procedia Comput Sci 113:344\u2013351","journal-title":"Procedia Comput Sci"},{"issue":"6","key":"594_CR16","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1370\/afm.3039","volume":"21","author":"A Emeryk","year":"2023","unstructured":"Emeryk A, Derom E, Janeczek K, Ku\u017anar-Kami\u0144ska B, Zelent A, \u0141ukaszyk M, Grzywalski Tomasz, Pastusiak Anna, Biniakowski Adam, Szarzy\u0144ski Krzysztof, Botteldooren Dick, Koci\u0144ski J\u0119drzej, Hafke-Dys H (2023) Home monitoring of asthma exacerbations in children and adults with use of an AI-aided stethoscope. Ann Fam Med 21(6):517\u2013525","journal-title":"Ann Fam Med"},{"key":"594_CR17","unstructured":"Goodfellow I (2016) Deep learning"},{"key":"594_CR18","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.73026.1","author":"R Haque","year":"2021","unstructured":"Haque R, Ho SB, Chai I, Abdullah A (2021) Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers. F1000Res. https:\/\/doi.org\/10.12688\/f1000research.73026.1","journal-title":"F1000Res"},{"issue":"4","key":"594_CR19","doi-asserted-by":"publisher","first-page":"ooad091","DOI":"10.1093\/jamiaopen\/ooad091","volume":"6","author":"N Hirons","year":"2023","unstructured":"Hirons N, Allen A, Matsuyoshi N, Su J, Kaye L, Barrett MA (2023) Prediction of short-acting beta-agonist usage in patients with asthma using temporal-convolutional neural networks. JAMIA Open 6(4):ooad091","journal-title":"JAMIA Open"},{"issue":"11","key":"594_CR20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0288903","volume":"18","author":"AA Huang","year":"2023","unstructured":"Huang AA, Huang SY (2023) Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population. PLoS One 18(11):e0288903","journal-title":"PLoS One"},{"issue":"1","key":"594_CR21","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-01847-0","volume":"22","author":"JH Hurst","year":"2022","unstructured":"Hurst JH, Zhao C, Hostetler HP, Ghiasi Gorveh M, Lang JE, Goldstein BA (2022) Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models. BMC Med Inform Decis Mak 22(1):108","journal-title":"BMC Med Inform Decis Mak"},{"issue":"7","key":"594_CR22","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1080\/02770903.2021.1923740","volume":"59","author":"S Hozawa","year":"2022","unstructured":"Hozawa S, Maeda S, Kikuchi A, Koinuma M (2022) Exploratory research on asthma exacerbation risk factors using the Japanese claims database and machine learning: a retrospective cohort study. J Asthma 59(7):1328\u20131337","journal-title":"J Asthma"},{"key":"594_CR23","doi-asserted-by":"publisher","first-page":"94601","DOI":"10.1109\/ACCESS.2021.3091487","volume":"9","author":"MR Islam","year":"2021","unstructured":"Islam MR, Moni MA, Islam MM, Rashed-Al-Mahfuz M, Islam MS, Hasan MK, Li\u00f3 P (2021) Emotion recognition from EEG signal focusing on deep learning and shallow learning techniques. IEEE Access 9:94601\u201394624","journal-title":"IEEE Access"},{"key":"594_CR24","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/978-3-031-69499-8_2","volume-title":"Shallow learning vs. Deep learning: A practical guide for machine learning solutions","author":"F Jafari","year":"2024","unstructured":"Jafari F, Moradi K, Shafiee Q (2024) Shallow learning vs. Deep learning in engineering applications. Shallow learning vs. Deep learning: A practical guide for machine learning solutions. Springer Nature Switzerland, Cham, pp 29\u201376"},{"key":"594_CR25","unstructured":"Jethani N et al (2021) FastSHAP: Real-time Shapley value Estimation. arXiv preprint arXiv:2107.10379"},{"key":"594_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12916-019-1426-2","volume":"17","author":"CJ Kelly","year":"2019","unstructured":"Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D (2019) Key challenges for delivering clinical impact with artificial intelligence. BMC Med 17:1\u20139","journal-title":"BMC Med"},{"issue":"1","key":"594_CR27","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.ccm.2018.10.004","volume":"40","author":"S Khurana","year":"2019","unstructured":"Khurana S, Jarjour NN (2019) Systematic approach to asthma of varying severity. Clin Chest Med 40(1):59\u201370","journal-title":"Clin Chest Med"},{"key":"594_CR28","doi-asserted-by":"crossref","unstructured":"Lee Y, Park Y, Kim C, Lee E, Lee HY, Woo SD, Park HS (2021) Longitudinal outcomes of severe asthma: real-world evidence of multidimensional analyses. The Journal of Allergy and Clinical Immunology: In Practice, 9(3), 1285\u20131294.","DOI":"10.1016\/j.jaip.2020.09.055"},{"issue":"1","key":"594_CR29","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-024-04866-9","volume":"22","author":"D Li","year":"2024","unstructured":"Li D, Abhadiomhen SE, Zhou D, Shen XJ, Shi L, Cui Y (2024) Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers. J Transl Med 22(1):100","journal-title":"J Transl Med"},{"key":"594_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.rmed.2021.106483","volume":"185","author":"K Lisspers","year":"2021","unstructured":"Lisspers K, St\u00e4llberg B, Larsson K, Janson C, M\u00fcller M, \u0141uczko M, Bjerregaard Bine Kj\u00f8ller, Bacher Gerald, Holzhauer Bj\u00f6rn, Goyal Pankaj, Johansson G (2021) Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients\u2019 data using machine learning-based on the ARCTIC study. Respir Med 185:106483","journal-title":"Respir Med"},{"key":"594_CR31","doi-asserted-by":"publisher","DOI":"10.2147\/JAA.S377631","author":"NL Lugogo","year":"2022","unstructured":"Lugogo NL, DePietro M, Reich M, Merchant R, Chrystyn H, Pleasants R, Granovsky Lena, Li Thomas, Hill Tanisha, Brown Randall W, Safioti G (2022) A predictive machine learning tool for asthma exacerbations: results from a 12-week, open-label study using an electronic multi-dose dry powder inhaler with integrated sensors. J Asthma Allergy. https:\/\/doi.org\/10.2147\/JAA.S377631","journal-title":"J Asthma Allergy"},{"issue":"1","key":"594_CR32","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz Ronit, Himmelfarb Jonathan, Bansal Nisha, Lee SI (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2(1):56\u201367","journal-title":"Nat Mach Intell"},{"key":"594_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-015-0208-9","volume":"15","author":"G Luo","year":"2015","unstructured":"Luo G, Stone BL, Fassl B, Maloney CG, Gesteland PH, Yerram SR, Nkoy FL (2015) Predicting asthma control deterioration in children. BMC Med Inform Decis Mak 15:1\u20138","journal-title":"BMC Med Inform Decis Mak"},{"key":"594_CR34","doi-asserted-by":"publisher","DOI":"10.2147\/JAA.S445450","author":"L Ma","year":"2024","unstructured":"Ma L, Tibble H (2024) Primary care asthma attack prediction models for adults: a systematic review of reported methodologies and outcomes. J Asthma Allergy. https:\/\/doi.org\/10.2147\/JAA.S445450","journal-title":"J Asthma Allergy"},{"key":"594_CR35","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.880","volume":"8","author":"R Mitchell","year":"2022","unstructured":"Mitchell R, Frank E, Holmes G (2022) GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles. PeerJ Comput Sci 8:e880","journal-title":"PeerJ Comput Sci"},{"key":"594_CR36","doi-asserted-by":"crossref","unstructured":"Modi K, Singh I, Kumar Y (2024) Predicting asthma control test score using machine learning regression models. In Next Generation Computing and Information Systems: Proceedings of the 2nd International Conference on Next Generation Computing and Information Systems (ICNGCIS 2023), December 18\u201319, 2023, Jammu, J&K, India (p. 190). CRC Press","DOI":"10.1201\/9781003466383-29"},{"key":"594_CR37","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/978-3-031-26845-8_8","volume-title":"Guide to cybersecurity in digital transformation: trends, methods, technologies, applications and best practices","author":"DP M\u00f6ller","year":"2023","unstructured":"M\u00f6ller DP (2023) Machine learning and deep learning. Guide to cybersecurity in digital transformation: trends, methods, technologies, applications and best practices. Springer Nature Switzerland, Cham, pp 347\u2013384"},{"key":"594_CR38","unstructured":"Napolitano D, Vaiani L, Cagliero L (2023) Learning confidence intervals for feature importance: A fast Shapley-based approach. In EDBT\/ICDT Workshops"},{"key":"594_CR39","doi-asserted-by":"crossref","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Moher D (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372.","DOI":"10.1136\/bmj.n71"},{"key":"594_CR40","doi-asserted-by":"crossref","unstructured":"Pan Z, Mishra P (2023) Hardware Acceleration of Explainable Artificial Intelligence. arXiv preprint arXiv:2305.04887","DOI":"10.1007\/978-3-031-46479-9_10"},{"issue":"4","key":"594_CR41","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1007\/s11739-021-02923-5","volume":"17","author":"O Para","year":"2022","unstructured":"Para O, Montagnani A, Guidi S, Bert\u00f9 L, Manfellotto D, Campanini M, FADOI-Epidemiological Study Group (2022) Hospitalization and mortality for acute exacerbation of asthma: an Italian population-based study. Intern Emerg Med 17(4):1107\u20131113","journal-title":"Intern Emerg Med"},{"issue":"4","key":"594_CR42","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s41060-023-00458-w","volume":"18","author":"SRA Parisineni","year":"2024","unstructured":"Parisineni SRA, Pal M (2024) Enhancing trust and interpretability of complex machine learning models using local interpretable model agnostic Shap explanations. Int J Data Sci Anal 18(4):457\u2013466","journal-title":"Int J Data Sci Anal"},{"issue":"12","key":"594_CR43","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1111\/acem.13655","volume":"25","author":"SJ Patel","year":"2018","unstructured":"Patel SJ, Chamberlain DB, Chamberlain JM (2018) A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage. Acad Emerg Med 25(12):1463\u20131470","journal-title":"Acad Emerg Med"},{"issue":"8","key":"594_CR44","doi-asserted-by":"publisher","first-page":"4012","DOI":"10.1109\/TIP.2018.2834830","volume":"27","author":"A Paul","year":"2018","unstructured":"Paul A, Mukherjee DP, Das P, Gangopadhyay A, Chintha AR, Kundu S (2018) Improved random forest for classification. IEEE Trans Image Process 27(8):4012\u20134024","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"594_CR45","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1016\/j.jaci.2022.09.041","volume":"151","author":"J Perez-Garcia","year":"2023","unstructured":"Perez-Garcia J, Gonz\u00e1lez-Carracedo M, Espuela-Ortiz A, Hern\u00e1ndez-P\u00e9rez JM, Gonz\u00e1lez-P\u00e9rez R, Sard\u00f3n-Prado O, Martin-Gonzalez Elena, Mederos-Luis Elena, Poza-Guedes Paloma, Corcuera-Elosegui Paula, Callero Ariel, S\u00e1nchez-Mach\u00edn Inmaculada, Korta-Murua Javier, P\u00e9rez-P\u00e9rez Jos\u00e9 A., Villar Jes\u00fas, Pino-Yanes Maria, Lorenzo-Diaz F (2023) The upper-airway microbiome as a biomarker of asthma exacerbations despite inhaled corticosteroid treatment. J Allergy Clin Immunol 151(3):706\u2013715","journal-title":"J Allergy Clin Immunol"},{"key":"594_CR46","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1629","volume":"9","author":"T Qamar","year":"2023","unstructured":"Qamar T, Bawany NZ (2023) Understanding the black-box: towards interpretable and reliable deep learning models. PeerJ Comput Sci 9:e1629","journal-title":"PeerJ Comput Sci"},{"issue":"1","key":"594_CR47","doi-asserted-by":"publisher","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","volume":"47","author":"SJ Rigatti","year":"2017","unstructured":"Rigatti SJ (2017) Random forest. J Insur Med 47(1):31\u201339","journal-title":"J Insur Med"},{"key":"594_CR48","unstructured":"Roelofs R, Shankar V, Recht B, Fridovich-Keil S, Hardt M, Miller J, Schmidt L (2019) A meta-analysis of overfitting in machine learning. Adv Neural Inf Process Syst, 32"},{"key":"594_CR49","doi-asserted-by":"crossref","unstructured":"Roshan K, Zafar A (2022), March Using kernel shap xai method to optimize the network anomaly detection model. In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 74\u201380). IEEE","DOI":"10.23919\/INDIACom54597.2022.9763241"},{"key":"594_CR50","unstructured":"Ryan R, Santesso N, Hill S (2016) Preparing Summary of Findings (SoF) tables. Cochrane Consumers and Communication Group"},{"issue":"4","key":"594_CR51","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s10664-024-10469-1","volume":"29","author":"L Schulte","year":"2024","unstructured":"Schulte L, Ledel B, Herbold S (2024) Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP. Empir Softw Eng 29(4):93","journal-title":"Empir Softw Eng"},{"key":"594_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01488-9","volume":"21","author":"S Secinaro","year":"2021","unstructured":"Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P (2021) The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak 21:1\u201323","journal-title":"BMC Med Inform Decis Mak"},{"key":"594_CR53","doi-asserted-by":"crossref","unstructured":"Silveira A, Mu\u00f1oz C, Mendoza L (2019) Severe asthma exacerbations prediction using neural networks. In Engineering Applications of Neural Networks: 20th International Conference, EANN 2019, Xersonisos, Crete, Greece, May 24\u201326, 2019, Proceedings 20 (pp. 115\u2013124). Springer International Publishing","DOI":"10.1007\/978-3-030-20257-6_10"},{"key":"594_CR54","doi-asserted-by":"publisher","DOI":"10.2196\/54556","volume":"26","author":"A Sriharan","year":"2024","unstructured":"Sriharan A, Sekercioglu N, Mitchell C, Senkaiahliyan S, Hertelendy A, Porter T, Banaszak-Holl J (2024) Leadership for AI transformation in health care organization: scoping review. J Med Internet Res 26:e54556","journal-title":"J Med Internet Res"},{"issue":"1","key":"594_CR55","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44\u201356","journal-title":"Nat Med"},{"issue":"3","key":"594_CR56","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1111\/1467-8551.00375","volume":"14","author":"D Tranfield","year":"2003","unstructured":"Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14(3):207\u2013222","journal-title":"Br J Manag"},{"key":"594_CR57","first-page":"700","volume":"316","author":"KC Tsang","year":"2024","unstructured":"Tsang KC (2024) Enhancing asthma self-management with environmental passive-monitoring data and machine learning-based predictions. Stud Health Technol Inform 316:700\u2013704","journal-title":"Stud Health Technol Inform"},{"key":"594_CR58","doi-asserted-by":"publisher","DOI":"10.2147\/JAA.S285742","author":"KC Tsang","year":"2022","unstructured":"Tsang KC, Pinnock H, Wilson AM, Shah SA (2022) Application of machine learning algorithms for asthma management with mHealth: a clinical review. J Asthma Allergy. https:\/\/doi.org\/10.2147\/JAA.S285742","journal-title":"J Asthma Allergy"},{"issue":"1","key":"594_CR59","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02241-9","volume":"10","author":"KC Tsang","year":"2023","unstructured":"Tsang KC, Pinnock H, Wilson AM, Salvi D, Shah SA (2023) Home monitoring with connected mobile devices for asthma attack prediction with machine learning. Sci Data 10(1):370","journal-title":"Sci Data"},{"key":"594_CR60","doi-asserted-by":"crossref","unstructured":"Vishnu VK, Rajput DS (2020) A review on the significance of machine learning for data analysis in big data. Jordanian J Computers Inform Technol, 6(1)","DOI":"10.5455\/jjcit.71-1564729835"},{"issue":"1","key":"594_CR61","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"Wolff RF, Moons KG, Riley RD, Whiting PF, Westwood M, Collins GS, Moons Karel G.M., Reitsma Johannes B., Kleijnen Jos, Mallett Sue, PROBAST Group\u2020 (2019) PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170(1):51\u201358","journal-title":"Ann Intern Med"},{"issue":"12","key":"594_CR62","doi-asserted-by":"publisher","first-page":"18077","DOI":"10.1007\/s11042-022-14226-8","volume":"82","author":"Y Wu","year":"2023","unstructured":"Wu Y, Shen XJ, Abhadiomhen SE, Yang Y, Gu JN (2023) Kernel ensemble support vector machine with integrated loss in shared parameters space. Multimedia Tools Appl 82(12):18077\u201318096","journal-title":"Multimedia Tools Appl"},{"issue":"7","key":"594_CR63","doi-asserted-by":"publisher","DOI":"10.2196\/16981","volume":"22","author":"Y Xiang","year":"2020","unstructured":"Xiang Y, Ji H, Zhou Y, Li F, Du J, Rasmy L, Wu Stephen, Zheng W Jim, Xu Hua, Zhi Degui, Zhang Yaoyun, Tao C (2020) Asthma exacerbation prediction and risk factor analysis based on a time-sensitive, attentive neural network: retrospective cohort study. J Med Internet Res 22(7):e16981","journal-title":"J Med Internet Res"},{"issue":"1","key":"594_CR64","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1186\/s12890-023-02570-w","volume":"23","author":"S Xiong","year":"2023","unstructured":"Xiong S, Chen W, Jia X, Jia Y, Liu C (2023) Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis. BMC Pulm Med 23(1):278","journal-title":"BMC Pulm Med"},{"key":"594_CR65","doi-asserted-by":"crossref","unstructured":"Xu M, Tantisira KG, Wu A, Litonjua AA, Chu JH, Himes BE, Weiss ST (2011) Genome Wide Association Study to predict severe asthma exacerbations in children using random forests classifiers. BMC medical genetics, 12, 1\u20138.","DOI":"10.1186\/1471-2350-12-90"},{"key":"594_CR66","unstructured":"Yang J (2021) Fast treeshap: accelerating Shap value computation for trees. ArXiv Preprint arXiv :210909847"},{"issue":"5","key":"594_CR67","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1016\/j.chest.2020.12.051","volume":"159","author":"JG Zein","year":"2021","unstructured":"Zein JG, Wu CP, Attaway AH, Zhang P, Nazha A (2021) Novel machine learning can predict acute asthma exacerbation. Chest 159(5):1747\u20131757","journal-title":"Chest"},{"issue":"9","key":"594_CR68","doi-asserted-by":"publisher","first-page":"11164","DOI":"10.1609\/aaai.v37i9.26322","volume":"37","author":"A Zern","year":"2023","unstructured":"Zern A, Broelemann K, Kasneci G (2023) Interventional SHAP values and interaction values for piecewise linear regression trees. Proceedings of the AAAI Conference on Artificial Intelligence 37(9):11164\u201311173","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"11","key":"594_CR69","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1080\/02770903.2020.1802746","volume":"58","author":"O Zhang","year":"2021","unstructured":"Zhang O, Minku LL, Gonem S (2021) Detecting asthma exacerbations using daily home monitoring and machine learning. J Asthma 58(11):1518\u20131527","journal-title":"J Asthma"},{"key":"594_CR70","doi-asserted-by":"publisher","DOI":"10.2196\/38220","volume":"10","author":"X Zhang","year":"2022","unstructured":"Zhang X (2022) Error and timeliness analysis for using machine learning to predict asthma hospital visits: retrospective cohort study. JMIR Med Inform 10:e38220","journal-title":"JMIR Med Inform"}],"container-title":["Network Modeling Analysis in Health Informatics and Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-025-00594-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13721-025-00594-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-025-00594-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T03:38:50Z","timestamp":1757475530000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13721-025-00594-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,8]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["594"],"URL":"https:\/\/doi.org\/10.1007\/s13721-025-00594-2","relation":{},"ISSN":["2192-6670"],"issn-type":[{"value":"2192-6670","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,8]]},"assertion":[{"value":"3 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2025","order":4,"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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics and consent to participate"}}],"article-number":"96"}}