{"id":"W4210661901","doi":"10.2196/26801","title":"Electronic Medical Record–Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Trade, Industry and Energy; Ministry of Food and Drug Safety; Korea Medical Device Development Fund","keywords":"Medicine; Receiver operating characteristic; Brier score; Random forest; Gradient boosting; Machine learning; Logistic regression; Percutaneous coronary intervention; Adverse effect; Artificial intelligence; Medical record; Emergency medicine; Internal medicine; Computer science; Myocardial infarction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00346207,0.0003242648,0.0004600615,0.0002264154,0.0006766788,0.00003191538,0.0009587373,0.0001637883,0.0001865496],"category_scores_gemma":[0.0009406362,0.0002369068,0.0001219806,0.0005086716,0.00008831671,0.0002609662,0.001040816,0.001977104,0.00001505141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004737419,"about_ca_system_score_gemma":0.001682238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001340568,"about_ca_topic_score_gemma":0.00002628025,"domain_scores_codex":[0.9943514,0.001307519,0.001027429,0.0003318325,0.002381015,0.0006007825],"domain_scores_gemma":[0.9976735,0.00072499,0.0005273613,0.0004651876,0.00009109764,0.0005178422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001965848,0.0005181973,0.373508,0.000563619,0.0003425652,0.00002284796,0.06781215,0.2832781,0.000001807036,0.0007659178,0.0001666561,0.2728235],"study_design_scores_gemma":[0.0009459309,0.000653723,0.007368932,0.0000756416,0.00003323082,0.0000203678,0.0004756947,0.9822133,0.00001856693,0.00006885239,0.007892014,0.0002337892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7678042,0.0003211179,0.2285369,0.0009643764,0.0002009159,0.001418694,0.00003581387,0.0002282995,0.0004896841],"genre_scores_gemma":[0.9824116,0.000215159,0.0152836,0.0007449654,0.00004500909,0.0008926893,0.0002340305,0.00002922495,0.0001437365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6989351,"threshold_uncertainty_score":0.9660781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137189307429773,"score_gpt":0.2598268465519188,"score_spread":0.2461079158089415,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}