{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T11:58:49Z","timestamp":1781697529314,"version":"3.54.5"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Medical Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.ijmedinf.2026.106489","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T01:27:27Z","timestamp":1778808447000},"page":"106489","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Development and validation of an interpretable machine learning model for early risk prediction of acute myocardial infarction"],"prefix":"10.1016","volume":"217","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1089-5584","authenticated-orcid":false,"given":"Shixuan","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6056-0514","authenticated-orcid":false,"given":"Longxiao","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7637-0820","authenticated-orcid":false,"given":"Ningji","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ijmedinf.2026.106489_b0005","doi-asserted-by":"crossref","unstructured":"A. Roth Gregory, C. Johnson, A. Abajobir, F. Abd-Allah, F. Abera Semaw, G. Abyu, M. Ahmed, B. Aksut, T. Alam, K. Alam, F. Alla, N. Alvis-Guzman, S. Amrock, H. Ansari, J. \u00c4rnl\u00f6v, H. Asayesh, M. Atey Tesfay, L. Avila-Burgos, A. Awasthi, A. Banerjee, A. Barac, T. B\u00e4rnighausen, L. Barregard, N. Bedi, E. Belay Ketema, D. Bennett, G. Berhe, Z. Bhutta, S. Bitew, J. Carapetis, J. Carrero Juan, C. Malta Deborah, A. Casta\u00f1eda-Orjuela Carlos, J. Castillo-Rivas, F. Catal\u00e1-L\u00f3pez, J.-Y. Choi, H. Christensen, M. Cirillo, L. Cooper, M. Criqui, D. Cundiff, A. Damasceno, L. Dandona, R. Dandona, K. Davletov, S. Dharmaratne, P. Dorairaj, M. Dubey, R. Ehrenkranz, M. El Sayed Zaki, A. Faraon Emerito Jose, A. Esteghamati, T. Farid, M. Farvid, V. Feigin, L. Ding Eric, G. Fowkes, T. Gebrehiwot, R. Gillum, A. Gold, P. Gona, R. Gupta, D. Habtewold Tesfa, N. Hafezi-Nejad, T. Hailu, B. Hailu Gessessew, G. Hankey, Y. Hassen Hamid, H. Abate Kalkidan, R. Havmoeller, I. Hay Simon, M. Horino, J. Hotez Peter, K. Jacobsen, S. James, M. Javanbakht, P. Jeemon, D. John, J. Jonas, Y. Kalkonde, C. Karimkhani, A. Kasaeian, Y. Khader, A. Khan, Y.-H. Khang, S. Khera, T. Khoja Abdullah, J. Khubchandani, D. Kim, D. Kolte, S. Kosen, J. Krohn Kristopher, G.A. Kumar, F. Kwan Gene, K. Lal Dharmesh, A. Larsson, S. Linn, A. Lopez, A. Lotufo Paulo, A. El Razek Hassan Magdy, R. Malekzadeh, M. Mazidi, T. Meier, G. Meles Kidanu, G. Mensah, A. Meretoja, H. Mezgebe, T. Miller, E. Mirrakhimov, S. Mohammed, E. Moran Andrew, I. Musa Kamarul, J. Narula, B. Neal, F. Ngalesoni, G. Nguyen, M. Obermeyer Carla, M. Owolabi, G. Patton, J. Pedro, D. Qato, M. Qorbani, K. Rahimi, K. Rai Rajesh, S. Rawaf, A. Ribeiro, S. Safiri, A. Salomon Joshua, I. Santos, M. Santric Milicevic, B. Sartorius, A. Schutte, S. Sepanlou, A. Shaikh Masood, M.-J. Shin, M. Shishehbor, H. Shore, S. Silva Diego Augusto, E. Sobngwi, S. Stranges, S. Swaminathan, R. Tabar\u00e9s-Seisdedos, N. Tadele Atnafu, F. Tesfay, J.S. Thakur, A. Thrift, R. Topor-Madry, T. Truelsen, S. Tyrovolas, N. Ukwaja Kingsley, O. Uthman, T. Vasankari, V. Vlassov, E. Vollset Stein, T. Wakayo, D. Watkins, R. Weintraub, A. Werdecker, R. Westerman, S. Wiysonge Charles, C. Wolfe, A. Workicho, G. Xu, Y. Yano, P. Yip, N. Yonemoto, M. Younis, C. Yu, T. Vos, M. Naghavi, C. Murray, Global, Regional, and National Burden of Cardiovascular Diseases for 10 Causes, 1990 to 2015, JACC, 70 (2017) 1-25.","DOI":"10.1016\/j.jacc.2017.04.052"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0010","doi-asserted-by":"crossref","first-page":"S42","DOI":"10.15441\/ceem.23.140","article-title":"Acute myocardial infarction diagnosed in emergency departments: a report from the National Emergency Department Information System (NEDIS) of Korea, 2018\u20132022","volume":"10","author":"Ahn","year":"2023","journal-title":"Clin. Exp. Emerg. Med."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0015","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1109\/JBHI.2018.2882518","article-title":"Adversarial MACE prediction after acute coronary syndrome using electronic health records","volume":"23","author":"Huang","year":"2019","journal-title":"IEEE J. Biomed. Health. Inf."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0020","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1016\/S0140-6736(11)60310-3","article-title":"A 2-h diagnostic protocol to assess patients with chest pain symptoms in the Asia-Pacific region (ASPECT): a prospective observational validation study","volume":"377","author":"Than","year":"2011","journal-title":"Lancet (London, England)"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0025","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1016\/S0140-6736(15)00391-8","article-title":"High-sensitivity cardiac troponin I at presentation in patients with suspected acute coronary syndrome: a cohort study","volume":"386","author":"Shah","year":"2015","journal-title":"Lancet (London, England)"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0030","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1161\/CIRCULATIONAHA.116.025021","article-title":"Comparison of the efficacy and safety of early rule-out pathways for acute myocardial infarction","volume":"135","author":"Chapman","year":"2017","journal-title":"Circulation"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0035","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1161\/CIRCULATIONAHA.119.042891","article-title":"A randomized trial of a 1-hour troponin T protocol in suspected acute coronary syndromes: design of the rapid assessment of possible acs in the emergency department with high sensitivity troponin T (RAPID-TnT) study","volume":"140","author":"Chew","year":"2019","journal-title":"Circulation"},{"issue":"2023","key":"10.1016\/j.ijmedinf.2026.106489_b0040","doi-asserted-by":"crossref","first-page":"3720","DOI":"10.1093\/eurheartj\/ehad191","article-title":"ESC guidelines for the management of acute coronary syndromes","volume":"44","author":"Byrne","year":"2023","journal-title":"Eur. Heart J."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0045","doi-asserted-by":"crossref","DOI":"10.1093\/ehjopen\/oeac048","article-title":"Rapid risk stratification of acute coronary syndrome: adoption of an adapted European Society of Cardiology 0\/1-hour troponin algorithm in a real-world setting","volume":"2","author":"Couch","year":"2022","journal-title":"Eur. Heart J. Open"},{"issue":"2022","key":"10.1016\/j.ijmedinf.2026.106489_b0050","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1016\/j.jacc.2022.08.750","article-title":"ACC expert consensus decision pathway on the evaluation and disposition of acute chest pain in the emergency department: a report of the American college of cardiology solution set oversight committee","volume":"80","author":"Kontos","year":"2022","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0055","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/TCST.2018.2849068","article-title":"Experimental modeling and identification of cardiac biomarkers release in acute myocardial infarction","volume":"28","author":"Procopio","year":"2020","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0060","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1001\/jamainternmed.2020.3989","article-title":"Accuracy of physicians' electrocardiogram interpretations a systematic review and meta-analysis","volume":"180","author":"Cook","year":"2020","journal-title":"JAMA Intern. Med."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0065","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.113.000268","article-title":"Physician accuracy in interpreting potential ST-segment elevation myocardial infarction electrocardiograms","volume":"2","author":"McCabe","year":"2013","journal-title":"J. Am. Heart Assoc."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0070","doi-asserted-by":"crossref","first-page":"189163","DOI":"10.1109\/ACCESS.2024.3491073","article-title":"Automated acute myocardial infarction detection using machine learning from phonocardiogram","volume":"12","author":"Puspasari","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0075","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.cvdhj.2023.06.001","article-title":"Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction","volume":"4","author":"Tarabanis","year":"2023","journal-title":"Cardiovasc. Digit. Health J."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0080","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1109\/JBHI.2022.3140433","article-title":"Machine learning based healthcare system for investigating the association between depression and quality of life","volume":"26","author":"Habib","year":"2022","journal-title":"IEEE J. Biomed. Health. Inf."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0085","doi-asserted-by":"crossref","first-page":"6017","DOI":"10.1109\/JBHI.2025.3554364","article-title":"ExSMART-PreRA: explainable survival and risk assessment using machine learning for time estimation in preclinical rheumatoid arthritis","volume":"29","author":"Salehi","year":"2025","journal-title":"IEEE J. Biomed. Health. Inf."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0090","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.1109\/JBHI.2024.3491593","article-title":"Randomized explainable machine learning models for efficient medical diagnosis","volume":"29","author":"Muhammad","year":"2025","journal-title":"IEEE J. Biomed. Health. Inf."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0095","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.bbe.2020.12.002","article-title":"Artificial intelligence and machine learning in emergency medicine","volume":"41","author":"Tang","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0100","doi-asserted-by":"crossref","first-page":"165","DOI":"10.15441\/ceem.22.366","article-title":"Artificial intelligence decision points in an emergency department","volume":"9","author":"Chang","year":"2022","journal-title":"Clin. Exp. Emerg. Med."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0105","doi-asserted-by":"crossref","first-page":"354","DOI":"10.15441\/ceem.23.145","article-title":"Explainable artificial intelligence in emergency medicine: an overview","volume":"10","author":"Okada","year":"2023","journal-title":"Clin. Exp. Emerg. Med."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0110","doi-asserted-by":"crossref","first-page":"108591","DOI":"10.1109\/ACCESS.2023.3321509","article-title":"Assessment of hematological predictors via explainable artificial intelligence in the prediction of acute myocardial infarction","volume":"11","author":"Yilmaz","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0115","doi-asserted-by":"crossref","first-page":"e729","DOI":"10.1016\/S2589-7500(24)00191-2","article-title":"Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study","volume":"6","author":"Toprak","year":"2024","journal-title":"The Lancet. Digital Health"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0120","doi-asserted-by":"crossref","unstructured":"M.P. Than, J.W. Pickering, Y. Sandoval, A.S.V. Shah, A. Tsanas, F.S. Apple, S. Blankenberg, L. Cullen, C. Mueller, J.T. Neumann, R. Twerenbold, D. Westermann, A. Beshiri, N.L. Mills, M.I.C. On behalf of the, P.M. George, A.M. Richards, R.W. Troughton, S.J. Aldous, A.R. Chapman, A. Anand, J. Greenslade, W. Parsonage, J. Boeddinghaus, K. Wildi, T. Nestelberger, P. Badertscher, S. Du, J. Huang, S.W. Smith, N.A. S\u00f6rensen, F. Ojeda, Machine learning to predict the likelihood of acute myocardial infarction, Circulation, 140 (2019) 899-909.","DOI":"10.1161\/CIRCULATIONAHA.119.041980"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0125","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1038\/s41591-023-02325-4","article-title":"Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations","volume":"29","author":"Doudesis","year":"2023","journal-title":"Nat. Med."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0130","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1093\/eurheartj\/ehaf004","article-title":"Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study","volume":"46","author":"Lee","year":"2025","journal-title":"Eur. Heart J."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0135","first-page":"104","article-title":"Handling missing values: a study of popular imputation packages in R, Knowl-based","volume":"160","author":"Yadav","year":"2018","journal-title":"System"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0140","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1007\/s10462-019-09709-4","article-title":"Missing value imputation: a review and analysis of the literature","volume":"53","author":"Lin","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0145","unstructured":"E. Hvitfeldt, Feature Engineering A-Z: Remove missing values, Retrieved [Access Date] from https:\/\/feaz-book.com\/remove-missing, (n.d.)."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0150","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1002\/sim.2993","article-title":"Youden Index and the optimal threshold for markers with mass at zero","volume":"27","author":"Schisterman","year":"2008","journal-title":"Stat. Med."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0155","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/s12874-024-02198-2","article-title":"Methods of determining optimal cut-point of diagnostic biomarkers with application of clinical data in ROC analysis: an update review","volume":"24","author":"Hassanzad","year":"2024","journal-title":"BMC Med. Res. Method."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0160","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1039\/D2VA00182A","article-title":"Machine learning based models for high-throughput classification of human pregnane X receptor activators","volume":"2","author":"Gou","year":"2023","journal-title":"Environ. Sci. Adv."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0165","unstructured":"Y. Gal, Z. Ghahramani, Dropout as a Bayesian approximation: representing model uncertainty in deep learning, In: Proceedings of the 33rd International Conference Machine Learning - Volume 48, JMLR.org, New York, NY, USA, 2016, pp. 1050\u20131059."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0170","doi-asserted-by":"crossref","first-page":"W422","DOI":"10.1093\/nar\/gkae236","article-title":"ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionalityand decision support","volume":"52","author":"Fu","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0175","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2025.106260","article-title":"Development and validation of interpretable machine learning models for dynamic prediction of prognosis in acute pancreatitis complicated by acute kidney injury: a multicenter study","volume":"209","author":"Bai","year":"2026","journal-title":"Int. J. Med. Inform."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0185","doi-asserted-by":"crossref","first-page":"6149","DOI":"10.1021\/acs.est.4c01201","article-title":"Graph convolutional network-enhanced model for screening persistent, mobile, and toxic and very persistent and very mobile substances","volume":"58","author":"Zhao","year":"2024","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0190","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s13321-019-0386-z","article-title":"A visual approach for analysis and inference of molecular activity spaces","volume":"11","author":"Kausar","year":"2019","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2026.106320","article-title":"Development of an interpretable machine learning model for predicting 4-year chronic kidney disease risk in elderly hypertensive patients","volume":"211","author":"Wang","year":"2026","journal-title":"Int. J. Med. Inform."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0200","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/j.jacc.2024.02.018","article-title":"Chest pain in the emergency department","volume":"83","author":"Kontos Michael","year":"2024","journal-title":"JACC"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0205","article-title":"Diagnostic performance of high-sensitivity cardiac troponin I in a multicenter U.S. emergency department cohort, JACC","volume":"2","author":"Mark","year":"2023","journal-title":"Advances"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0210","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.jacc.2024.10.076","article-title":"Diagnostic and prognostic performance of high-sensitivity cardiac troponin T vs I","volume":"85","author":"Koechlin","year":"2025","journal-title":"JACC"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.amjmed.2018.10.002","article-title":"Prevalence, determinants, and clinical associations of high-sensitivity cardiac troponin in patients attending emergency departments","volume":"132","author":"Lee","year":"2019","journal-title":"Am. J. Med."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0220","first-page":"577","article-title":"Editor's choice-what to do when you question cardiac troponin values","volume":"7","author":"Mair","year":"2018","journal-title":"Eur Heart J-Acute Ca"},{"key":"10.1016\/j.ijmedinf.2026.106489_b0225","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","article-title":"Clustering-based undersampling in class-imbalanced data","volume":"409\u2013410","author":"Lin","year":"2017","journal-title":"Inform Sci."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0230","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1109\/TII.2021.3084132","article-title":"A novel imbalanced data classification method based on weakly supervised learning for fault diagnosis","volume":"18","author":"Liu","year":"2022","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0235","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10462-024-10759-6","article-title":"A survey on imbalanced learning: latest research, applications and future directions","volume":"57","author":"Chen","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.ijmedinf.2026.106489_b0240","doi-asserted-by":"crossref","unstructured":"S. Kar, K. Roy, J. Leszczynski, Applicability domain: A step toward confident predictions and decidability for QSAR modeling, Methods in molecular biology (Clifton, N.J.), 1800 (2018) 141-169.","DOI":"10.1007\/978-1-4939-7899-1_6"}],"container-title":["International Journal of Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505626002297?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386505626002297?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T11:35:39Z","timestamp":1781696139000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1386505626002297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":48,"alternative-id":["S1386505626002297"],"URL":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2026.106489","relation":{},"ISSN":["1386-5056"],"issn-type":[{"value":"1386-5056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Development and validation of an interpretable machine learning model for early risk prediction of acute myocardial infarction","name":"articletitle","label":"Article Title"},{"value":"International Journal of Medical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijmedinf.2026.106489","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106489"}}