{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T18:04:00Z","timestamp":1784225040140,"version":"3.55.0"},"reference-count":18,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>\n                    Cardiovascular diseases (CVDs) remain a leading source of morbidity, mortality, and healthcare burden worldwide. In patients with coronary artery disease (CAD), choosing among medical therapy, percutaneous coronary intervention (PCI), and coronary artery bypass grafting (CABG) is clinically complex and depends on multiple pre-procedural factors. In this retrospective multicenter registry study, we developed a multiclass machine learning model to predict\n                    <jats:italic>physician-assigned treatment categories<\/jats:italic>\n                    rather than clinically optimal treatment. The analytic cohort included 2,682 unique patient records from the BioArc Clinical Registry, with class imbalance across medical therapy (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20091,634), PCI (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009817), and surgery (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009231). Semi-structured JSON records were parsed to extract pre-decision demographic, clinical, symptom-related, and risk-factor variables. Missing data handling, encoding, SMOTE, recursive feature elimination (RFE), and hyperparameter tuning were performed within a nested stratified cross-validation workflow to reduce dependence on a single data split and to limit information leakage. The final XGBoost workflow retained\n                    <jats:bold>18<\/jats:bold>\n                    predictors after RFE. Across the outer folds of nested cross-validation, the model achieved an overall accuracy of\n                    <jats:bold>82.3%\u2009\u00b1\u20091.5%<\/jats:bold>\n                    (95% CI:\n                    <jats:bold>80.8%\u201383.8%<\/jats:bold>\n                    ) and a macro F1-score of\n                    <jats:bold>0.775\u2009\u00b1\u20090.018<\/jats:bold>\n                    (95% CI:\n                    <jats:bold>0.757\u20130.793<\/jats:bold>\n                    ), with one-vs-rest AUC values of\n                    <jats:bold>0.78\u2009\u00b1\u20090.02<\/jats:bold>\n                    ,\n                    <jats:bold>0.75\u2009\u00b1\u20090.02<\/jats:bold>\n                    , and\n                    <jats:bold>0.80\u2009\u00b1\u20090.03<\/jats:bold>\n                    for medical therapy, PCI, and surgery, respectively. Class-specific performance and confusion patterns were summarized from outer-fold predictions. Model interpretation using SHAP and partial dependence plots identified age, systolic blood pressure, symptom-related features, and opium consumption as influential predictors in this dataset. These findings indicate that pre-procedural EMR data contain signal related to observed treatment-assignment patterns; however, the model does not identify the optimal treatment or demonstrate improved clinical outcomes. External validation, external calibration, and prospective outcome-based evaluation are required before clinical deployment.\n                  <\/jats:p>","DOI":"10.1371\/journal.pdig.0001149","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T17:22:34Z","timestamp":1784222554000},"page":"e0001149","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning prediction of physician-assigned treatment categories from pre-procedural electronic medical record data in cardiac patients: A multiclass classification study"],"prefix":"10.1371","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5089-3917","authenticated-orcid":true,"given":"Mohammad","family":"Tanhaei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"issue":"8","key":"pdig.0001149.ref001","doi-asserted-by":"crossref","DOI":"10.1161\/CIR.0000000000000950","article-title":"Heart Disease and Stroke Statistics\u20142021 Update: A Report From the American Heart Association","volume":"143","author":"SS Virani","year":"2021","journal-title":"Circulation"},{"issue":"2","key":"pdig.0001149.ref002","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1093\/eurheartj\/ehy394","article-title":"2018 ESC\/EACTS Guidelines on Myocardial Revascularization","volume":"40","author":"FJ Neumann","year":"2019","journal-title":"Eur Heart J"},{"issue":"3","key":"pdig.0001149.ref003","article-title":"2021 ACC\/AHA\/SCAI Guideline for Coronary Artery Revascularization: A Report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines","volume":"145","author":"JS Lawton","year":"2022","journal-title":"Circulation"},{"issue":"4","key":"pdig.0001149.ref004","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1093\/ejcts\/ezs043","article-title":"EuroSCORE II","volume":"41","author":"SAM Nashef","year":"2012","journal-title":"Eur J Cardio-Thorac Surg"},{"issue":"23","key":"pdig.0001149.ref005","doi-asserted-by":"crossref","first-page":"2668","DOI":"10.1016\/j.jacc.2018.03.521","article-title":"Artificial Intelligence in Cardiology","volume":"71","author":"KW Johnson","year":"2018","journal-title":"J Am Coll Cardiol"},{"issue":"14","key":"pdig.0001149.ref006","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1136\/heartjnl-2017-311198","article-title":"Machine Learning in Cardiovascular Medicine: Are We There Yet?","volume":"104","author":"K Shameer","year":"2018","journal-title":"Heart"},{"issue":"21","key":"pdig.0001149.ref007","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1016\/j.jacc.2017.03.571","article-title":"Artificial Intelligence in Precision Cardiovascular Medicine","volume":"69","author":"C Krittanawong","year":"2017","journal-title":"J Am Coll Cardiol"},{"key":"pdig.0001149.ref008","doi-asserted-by":"crossref","first-page":"1795588","DOI":"10.1155\/2022\/1795588","article-title":"Using Machine Learning to Predict the Requirement for Revascularization in Patients with Chest Pain in the Emergency Department","volume":"2022","author":"Z Zheng","year":"2022","journal-title":"J Healthc Eng"},{"issue":"22","key":"pdig.0001149.ref009","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1016\/j.jacc.2023.09.818","article-title":"Can machine learning aid the selection of percutaneous vs surgical revascularization?","volume":"82","author":"K Ninomiya","year":"2023","journal-title":"J Am Coll Cardiol"},{"key":"pdig.0001149.ref010","article-title":"Comprehensive Machine Learning-Enabled Outcome Prediction for Patients With Coronary Artery Disease Using Multicentre Patient Data","author":"E Bogner","year":"2025","journal-title":"Can J Cardiol"},{"issue":"6","key":"pdig.0001149.ref011","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.ihj.2020.10.003","article-title":"Opium and cardiovascular health: a devil or an angel?","volume":"72","author":"P Roayaei","year":"2020","journal-title":"Indian Heart J"},{"issue":"1","key":"pdig.0001149.ref012","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s12906-023-03833-z","article-title":"Effects of opium use on one-year major adverse cardiovascular events (MACE) in the patients with ST-segment elevation MI undergoing primary PCI: a propensity score matched - machine learning based study","volume":"23","author":"Y Jenab","year":"2023","journal-title":"BMC Complement Med Ther"},{"issue":"1","key":"pdig.0001149.ref013","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1093\/eurjpc\/zwaa006","article-title":"Long-term opiate use and risk of cardiovascular mortality: results from the Golestan Cohort Study","volume":"28","author":"M Nalini","year":"2021","journal-title":"Eur J Prev Cardiol"},{"issue":"1","key":"pdig.0001149.ref014","doi-asserted-by":"crossref","first-page":"38","DOI":"10.5334\/gh.1315","article-title":"Long-Term Effects of Opium Consumption Following Percutaneous Coronary Intervention: A 10-Year Follow-Up Study","volume":"19","author":"A Izadi Amoli","year":"2024","journal-title":"Global Heart"},{"key":"pdig.0001149.ref015","article-title":"Association between chronic opioid exposure and cardiovascular disease: a systematic review and meta-analysis","author":"J Schofield","year":"2025","journal-title":"Eur J Prev Cardiol"},{"key":"pdig.0001149.ref016","unstructured":"BioArc. BioArc: A Platform for Creating Electronic Health Records for Patients and a Smart Assistant for Physicians. 2024. Available from: https:\/\/bioarc.ir\/landing\/index?lang=en"},{"key":"pdig.0001149.ref017","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 785\u201394.","DOI":"10.1145\/2939672.2939785"},{"key":"pdig.0001149.ref018","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"NV Chawla","year":"2002","journal-title":"jair"}],"container-title":["PLOS Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0001149","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T17:22:39Z","timestamp":1784222559000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0001149"}},"subtitle":[],"editor":[{"given":"Dhiya","family":"Al-Jumeily OBE","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"references-count":18,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,16]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pdig.0001149","relation":{},"ISSN":["2767-3170"],"issn-type":[{"value":"2767-3170","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]}}}