{ "abstract": "In-hospital adverse events after acute myocardial infarction (AMI) can occur abruptly and adversely affect short-term prognosis. We developed and externally validated explainable machine learning models to predict a composite of in-hospital atrial fibrillation (AF), ventricular fibrillation (VF), cardiac arrest (CA), or death using routinely available admission data. We retrospectively analyzed 7,496 patients from the MIMIC-IV database as the development cohort and 2,776 patients from Xuanwu Hospital as the external validation cohort. Predictors included demographics, medical history, and admission laboratory tests. Logistic regression, random forest, extreme gradient boosting (XGBoost), multilayer perceptron, and TabNet models were evaluated using five-fold cross-validation. Performance was assessed using discrimination, classification, calibration, and decision curve analysis. Latent class analysis identified three outcome clusters with distinct event-probability profiles. XGBoost achieved the best performance for the composite outcome in the development cohort (AUROC, 0.798). Predictive performance was moderate to good for AF and in-hospital death, with AUROCs of up to 0.834 and 0.870 in the development cohort and 0.803 and 0.849 in the external validation cohort, respectively. Discrimination was lower for VF and CA. SHapley Additive exPlanations highlighted neutrophil and leukocyte counts, N-terminal pro-B-type natriuretic peptide, renal function, glucose, glycated hemoglobin A1c, and prior AF as important predictors. These explainable models may support early risk stratification and help guide monitoring intensity and timely intervention in patients with AMI. Author summary: Acute myocardial infarction (AMI) is a life-threatening cardiovascular emergency associated with a high risk of severe in-hospital adverse events, including atrial fibrillation, ventricular fibrillation, cardiac arrest, and death. Early and accurate risk stratification using routinely available admission data remains a major clinical challenge. In this two-center retrospective study, we developed and externally validated explainable artificial intelligence models for predicting multiple in-hospital adverse events in patients with AMI. Latent class analysis was applied to identify heterogeneous phenotypic clusters of adverse events, while SHapley Additive exPlanations were used to enhance model interpretability. Our results demonstrated that tree-based models, particularly XGBoost and random forest, achieved favorable predictive performance for both composite and individual adverse events across development and external validation cohorts. Key predictors included inflammatory markers, cardiac stress indicators, renal function parameters, and metabolic biomarkers. These explainable models may support early admission-stage risk stratification, facilitate individualized monitoring strategies, and assist timely clinical intervention for hospitalized AMI patients. Overall, this study provides a clinically applicable, data-driven framework for improving inpatient risk management and optimizing resource allocation in high-risk cardiovascular care.", "abstract_shown": true, "affiliations": [ "School of Biomedical Engineering, Capital Medical University, Beijing, China", "Beijing Key Laboratory of Clinical Engineering Solutions for Mental Health, Capital Medical University, Beijing, China", "Department of Cardiology, Xuanwu Hospital, Capital Medical University, Beijing, China" ], "article_type": "Research Article", "arxiv": null, "author_total": 10, "authors": [ { "affs": [ 0 ], "name": "Xican Tian" }, { "affs": [ 1 ], "name": "Mengyuan Ji" }, { "affs": [ 2 ], "name": "Tao Wang" }, { "name": "Yingliang Yang" }, { "name": "Pixian Yuan" }, { "name": "Yang Zhang" }, { "name": "Congmin Zhu" }, { "name": "Jinggang Xia" }, { "name": "Yu Gu" }, { "name": "Honglei Liu" } ], "bibtex": "https://research.draconitic.com/paper/2jvf2xa0fynzd.bib", "cited_by": 0, "doi": "10.1371/journal.pdig.0001812", "fulltext_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001812", "id": "2jvf2xa0fynzd", "issns": [ "2767-3170" ], "landing_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001812", "language": "eng", "licence": "CC-BY", "licence_label": "CC BY", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "plos", "/Biology and life sciences/Biochemistry/Biomarkers/Creatinine" ], [ "plos", "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Blood cells/White blood cells/Neutrophils" ], [ "plos", "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Immune cells/White blood cells/Neutrophils" ], [ "plos", "/Biology and life sciences/Immunology/Immune cells/White blood cells/Neutrophils" ], [ "plos", "/Biology and life sciences/Immunology/Immune response/Inflammation" ], [ "plos", "/Medicine and health sciences/Cardiology/Arrhythmia/Atrial fibrillation" ], [ "plos", "/Medicine and health sciences/Cardiology/Cardiovascular medicine/Cardiovascular diseases/Cardiovascular disease risk" ], [ "plos", "/Medicine and health sciences/Cardiology/Myocardial infarction" ], [ "plos", "/Medicine and health sciences/Clinical medicine/Signs and symptoms/Inflammation" ], [ "plos", "/Medicine and health sciences/Epidemiology/Medical risk factors" ], [ "plos", "/Medicine and health sciences/Immunology/Immune cells/White blood cells/Neutrophils" ], [ "plos", "/Medicine and health sciences/Immunology/Immune response/Inflammation" ], [ "plos", "/Medicine and health sciences/Medical conditions/Cardiovascular diseases/Cardiovascular disease risk" ], [ "plos", "/Research and analysis methods/Research design/Clinical research design/Adverse events" ] ], "openalex": null, "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001812&type=printable", "pmcid": "PMC13754119", "pmid": null, "published": "2026-11-01", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/2jvf2xa0fynzd.ris", "sources": [ { "first_seen": "2026-11-12T12:20:55Z", "native_id": "10.1371/journal.pdig.0001812", "source": "plos", "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001812" }, { "first_seen": "2026-11-12T15:23:18Z", "native_id": "PMC:PMC13754119", "source": "europepmc", "url": "https://europepmc.org/article/PMC/PMC13754119" } ], "subjects": [ { "field": "biology", "origin": "plos:cell biology", "subfield": "cell-biology" }, { "field": "biology", "origin": "plos:immunology", "subfield": "immunology" }, { "field": "medicine", "origin": "plos:cardiology", "subfield": "cardiovascular" } ], "title": "Development and validation of an explainable artificial intelligence model to predict in-hospital adverse events for patients with acute myocardial infarction", "title_text": "Development and validation of an explainable artificial intelligence model to predict in-hospital adverse events for patients with acute myocardial infarction", "type": "peer-reviewed", "updated_at": "2026-11-14T03:20:06Z", "url": "https://research.draconitic.com/paper/2jvf2xa0fynzd", "venue": "PLOS Digital Health", "versions": [] }