{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T01:59:05Z","timestamp":1790560745191,"version":"4.1.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Tianshan Innovation Team Program of Autonomous Region","award":["2022D14007"],"award-info":[{"award-number":["2022D14007"]}]},{"name":"Tianshan Innovation Team Program of Autonomous Region","award":["2022D14007"],"award-info":[{"award-number":["2022D14007"]}]},{"name":"Health Kashgar National Regional Medical Center Talent Cultivation Demonstration Base","award":["KSRC-2022001"],"award-info":[{"award-number":["KSRC-2022001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Infect Dis"],"DOI":"10.1186\/s12879-024-09771-6","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T08:02:23Z","timestamp":1724832143000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting hospitalization costs for pulmonary tuberculosis patients based on machine learning"],"prefix":"10.1186","volume":"24","author":[{"given":"Shiyu","family":"Fan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abudoukeyoumujiang","family":"Abulizi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chencui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasen","family":"Yimit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiange","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoguang","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayidili","family":"Nijiati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"9771_CR1","doi-asserted-by":"publisher","first-page":"S7","DOI":"10.1016\/j.ijid.2021.02.107","volume":"113","author":"J Chakaya","year":"2021","unstructured":"Chakaya J, Khan M, Ntoumi F, Aklillu E, Fatima R, Mwaba P, et al. Global Tuberculosis Report 2020\u2013Reflections on the Global TB burden, treatment and prevention efforts. Int J Infect Dis. 2021;113:S7\u201312.","journal-title":"Int J Infect Dis"},{"issue":"03","key":"9771_CR2","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1055\/s-0038-1651492","volume":"39","author":"G Philippe","year":"2018","unstructured":"Philippe G, Katherine F, Mario R. Iconography: global epidemiology of tuberculosis. Seminars Respir Crit Care Med. 2018;39(03):271\u201385.","journal-title":"Seminars Respir Crit Care Med"},{"issue":"12","key":"9771_CR3","doi-asserted-by":"publisher","first-page":"181","DOI":"10.46234\/ccdcw2020.048","volume":"2","author":"W Chen","year":"2020","unstructured":"Chen W, Zhang H, Du X, Li T, Zhao Y. Characteristics and Morbidity of the Tuberculosis Epidemic-China, 2019. China CDC Weekly. 2020;2(12):181\u20134.","journal-title":"China CDC Weekly"},{"key":"9771_CR4","unstructured":"World Health Organization. World health statistics 2021: monitoring health for the SDGs, sustainable development goals.\u00a0World Health Organ. 2021;2021:1\u2013136."},{"issue":"1","key":"9771_CR5","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1038\/s41746-020-00354-8","volume":"3","author":"I Osawa","year":"2020","unstructured":"Osawa I, Goto T, Yamamoto Y, Tsugawa Y. Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data. NPJ Digit Med. 2020;3(1):148.","journal-title":"NPJ Digit Med"},{"issue":"1","key":"9771_CR6","doi-asserted-by":"publisher","first-page":"e1918962","DOI":"10.1001\/jamanetworkopen.2019.18962","volume":"3","author":"RJ Desai","year":"2020","unstructured":"Desai RJ, Wang SV, Vaduganathan M. Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes. JAMA Netw Open. 2020;3(1):e1918962.","journal-title":"JAMA Netw Open"},{"key":"9771_CR7","doi-asserted-by":"publisher","first-page":"7969220","DOI":"10.1155\/2022\/7969220","volume":"2022","author":"AI Taloba","year":"2022","unstructured":"Taloba AI, El-Aziz A, Rasha M, El-Bagoury AAH. Estimation and prediction of hospitalization and medical care costs using regression in machine learning. J Healthc Eng. 2022;2022:7969220.","journal-title":"J Healthc Eng"},{"issue":"12","key":"9771_CR8","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1016\/j.jse.2022.07.013","volume":"31","author":"AK Gowd","year":"2022","unstructured":"Gowd AK, Agarwalla A, Beck EC, Rosas S, Waterman BR, Romeo AA, et al. Prediction of total healthcare cost following total shoulder arthroplasty utilizing machine learning. J Shoulder Elbow Surg. 2022;31(12):2449\u201356.","journal-title":"J Shoulder Elbow Surg"},{"issue":"2","key":"9771_CR9","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.carj.2018.02.002","volume":"69","author":"A Tang","year":"2018","unstructured":"Tang A, Tam R, Cadrin-Ch\u00eanevert A, Guest W, Chong J, Barfett J, et al. Canadian association of radiolo-gists white paper on artificial intelligence in radiology. Can Assoc Radiol J. 2018;69(2):120\u201335.","journal-title":"Can Assoc Radiol J"},{"issue":"7639","key":"9771_CR10","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Der-matologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115.","journal-title":"Nature"},{"issue":"1","key":"9771_CR11","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","volume":"1","author":"A Rajkomar","year":"2018","unstructured":"Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Dig Med. 2018;1(1):18.","journal-title":"NPJ Dig Med"},{"issue":"6","key":"9771_CR12","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/s41551-023-01045-x","volume":"7","author":"HY Zhou","year":"2023","unstructured":"Zhou HY, Yu Y, Wang C, Zhang S, Gao Y, Pan J, et al. A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. Nat Biomed Eng. 2023;7(6):743\u201355.","journal-title":"Nat Biomed Eng"},{"key":"9771_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ceh.2020.11.002","volume":"4","author":"M Desai","year":"2021","unstructured":"Desai M, Shah M. An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clin eHealth. 2021;4:1\u201311.","journal-title":"Clin eHealth"},{"issue":"14","key":"9771_CR14","doi-asserted-by":"publisher","first-page":"4036","DOI":"10.3390\/s20144036","volume":"20","author":"S Mishra","year":"2020","unstructured":"Mishra S, Tripathy HK, Mallick PK, Bhoi AK, Barsocchi P. EAGA-MLP\u2014an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors. 2020;20(14):4036.","journal-title":"Sensors"},{"key":"9771_CR15","doi-asserted-by":"crossref","unstructured":"Poongodi M, Malviya M, Kumar C, Hamdi M, Vijayakumar V, Nebhen J, et al. New York City taxi trip duration prediction using MLP and XGBoost. Int J Syst Assurance Eng Manage. 2022;1:1\u201312.","DOI":"10.1007\/s13198-021-01130-x"},{"key":"9771_CR16","doi-asserted-by":"publisher","first-page":"103565","DOI":"10.1016\/j.jbi.2020.103565","volume":"111","author":"MA Morid","year":"2020","unstructured":"Morid MA, Sheng ORL, Kawamoto K, Abdelrahman S. Learning hidden patterns from patient multivariate time series data using convolutional neural networks: A case study of healthcare cost prediction. J Biomed Inform. 2020;111:103565.","journal-title":"J Biomed Inform"},{"issue":"1","key":"9771_CR17","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s12911-021-01743-z","volume":"22","author":"P Drewe-Boss","year":"2022","unstructured":"Drewe-Boss P, Enders D, Walker J, Ohler U. Deep learning for prediction of population health costs. BMC Med Inform Decis Mak. 2022;22(1):32.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"8","key":"9771_CR18","doi-asserted-by":"publisher","first-page":"1208","DOI":"10.3390\/jpm12081208","volume":"12","author":"A Al Bataineh","year":"2022","unstructured":"Al Bataineh A, Manacek S. MLP-PSO hybrid algorithm for heart disease prediction. J Personal Med. 2022;12(8):1208.","journal-title":"J Personal Med"},{"key":"9771_CR19","doi-asserted-by":"publisher","first-page":"e20200015","DOI":"10.36416\/1806-3756\/e20200015","volume":"46","author":"ASR Moreira","year":"2020","unstructured":"Moreira ASR, Kritski AL, Carvalho ACC. Social determinants of health and catastrophic costs associated with the diagnosis and treatment of tuberculosis. J Bras Pneumol. 2020;46:e20200015.","journal-title":"J Bras Pneumol"},{"issue":"2","key":"9771_CR20","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.ijtb.2022.05.005","volume":"70","author":"R Samuel","year":"2023","unstructured":"Samuel R, Natesan S, Bangera MK. Quality of life and associating factors in pulmonary tuberculosis patients. Indian Journal of Tuberculosis. 2023;70(2):214\u201321.","journal-title":"Indian Journal of Tuberculosis"},{"issue":"6","key":"9771_CR21","doi-asserted-by":"publisher","first-page":"e036892","DOI":"10.1136\/bmjopen-2020-036892","volume":"10","author":"LF Assebe","year":"2020","unstructured":"Assebe LF, Negussie EK, Jbaily A, Tolla MTT. Financial burden of HIV and TB among patients in Ethiopia: a cross-sectional survey. BMJ Open. 2020;10(6):e036892.","journal-title":"BMJ Open."},{"issue":"03","key":"9771_CR22","first-page":"31","volume":"9","author":"XZ Li","year":"2020","unstructured":"Li XZ, Jin F, Zhang JG, Deng YF, Shu W, Qin JM, et al. Treatment of coronavirus disease 2019 in Shandong, China: a cost and affordability analysis. Infect Dis Poverty. 2020;9(03):31\u20138.","journal-title":"Infect Dis Poverty"},{"issue":"7","key":"9771_CR23","doi-asserted-by":"publisher","first-page":"e002280","DOI":"10.1136\/bmjgh-2019-002280","volume":"5","author":"C Oga-Omenka","year":"2020","unstructured":"Oga-Omenka C, Tseja-Akinrin A, Sen P. Factors influencing diagnosis and treatment initiation for multidrug-resistant\/rifampicin-resistant tuberculosis in six sub-Saharan African countries: a mixed-methods systematic review. BMJ Global Health. 2020;5(7):e002280.","journal-title":"BMJ Global Health."},{"issue":"28","key":"9771_CR24","doi-asserted-by":"publisher","first-page":"e21023","DOI":"10.1097\/MD.0000000000021023","volume":"99","author":"Y Wang","year":"2020","unstructured":"Wang Y, McNeil EB, Huang Z, Chen L, Lu X, Wang C. Household financial burden among multidrug-resistant tuberculosis patients in Guizhou province, China: a cross-sectional study. Medicine. 2020;99(28):e21023.","journal-title":"Medicine"},{"issue":"8","key":"9771_CR25","doi-asserted-by":"publisher","first-page":"e37578","DOI":"10.2196\/37578","volume":"10","author":"D Gopukumar","year":"2022","unstructured":"Gopukumar D, Ghoshal A, Zhao H. Predicting readmission charges billed by hospitals: machine learning approach. JMIR Med Inform. 2022;10(8):e37578.","journal-title":"JMIR Med Inform"},{"issue":"16","key":"9771_CR26","doi-asserted-by":"publisher","first-page":"e6248","DOI":"10.1002\/cpe.6248","volume":"33","author":"P Theerthagiri","year":"2021","unstructured":"Theerthagiri P. Forecasting hyponatremia in hospitalized patients using multilayer perceptron and multivariate linear regression techniques. Concurr Comput: Pract Exp. 2021;33(16):e6248.","journal-title":"Concurr Comput: Pract Exp"},{"issue":"1","key":"9771_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12913-021-07334-y","volume":"21","author":"M Chen","year":"2021","unstructured":"Chen M, Wu X, Zhang J, Dong E. Prediction of total hospital expenses of patients undergoing breast cancer surgery in Shanghai, China by comparing three models. BMC Health Serv Res. 2021;21(1):1\u20139.","journal-title":"BMC Health Serv Res"},{"key":"9771_CR28","doi-asserted-by":"crossref","unstructured":"Miranda AC, Santana JCC, Yamamura CLK, Rosa JM, Tambourgi EB, Ho LL, et al. Application of neural network to simulate the behavior of hospitalizations and their costs under the effects of various polluting gases in the city of S\u00e3o Paulo.\u00a0Air Qual Atmos\u00a0Health. 2021;1:1\u20139.","DOI":"10.1007\/s11869-021-01077-9"},{"key":"9771_CR29","doi-asserted-by":"crossref","unstructured":"Karnuta JM, Navarro SM, Haeberle HS, Helm JM, Kamath AF, Schaffer JL, et al. Predicting inpatient payments prior to lower extremity arthroplasty using deep learning: which model architecture is best? J Arthroplasty. 2019;34(10):2235\u201341.","DOI":"10.1016\/j.arth.2019.05.048"},{"issue":"1","key":"9771_CR30","doi-asserted-by":"publisher","first-page":"8867190","DOI":"10.1155\/2021\/8867190","volume":"2021","author":"Y Chen","year":"2021","unstructured":"Chen Y, Chen S, Zhang N, Liu H, Jing H, Min G. LPR-MLP: A novel health prediction model for transmission lines in grid sensor networks. Complexity. 2021;2021(1):8867190.","journal-title":"Complexity"},{"key":"9771_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8886170","volume":"2020","author":"D Car-Pusic","year":"2020","unstructured":"Car-Pusic D, Petruseva S, Zileska Pancovska V, Zafirovski Z. Neural network-based model for predicting preliminary construction cost as part of cost predicting system. Adv Civil Eng. 2020;2020:1\u201313.","journal-title":"Adv Civil Eng"},{"issue":"6","key":"9771_CR32","first-page":"5229","volume":"11","author":"RRK Al-Taie","year":"2021","unstructured":"Al-Taie RRK, Saleh BJ, Saedi AYF, Salman LA. Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: a case study in Iraq. Int J Electr Comput Eng. 2021;11(6):5229.","journal-title":"Int J Electr Comput Eng"}],"container-title":["BMC Infectious Diseases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12879-024-09771-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12879-024-09771-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12879-024-09771-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T08:03:46Z","timestamp":1724832226000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcinfectdis.biomedcentral.com\/articles\/10.1186\/s12879-024-09771-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,28]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["9771"],"URL":"https:\/\/doi.org\/10.1186\/s12879-024-09771-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4186975\/v1","asserted-by":"object"}]},"ISSN":["1471-2334"],"issn-type":[{"value":"1471-2334","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,28]]},"assertion":[{"value":"29 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2024","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 1964 Declaration of Helsinki and its later amendments or equivalent ethical standards were followed in all procedures carried out in studies involving human subjects. This retrospective study was approved by the Ethics Committee of The First People\u2019 s Hospital of Kashi (Kashgar) Prefecture 2023\u201360, which waived the need for written informed consent from the patients.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"875"}}