{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01483644,0.0009292339,0.0008396739,0.004296915,0.0003651007,0.001508343,0.001492832,0.001056771,0.001185808],"category_scores_gemma":[0.02770167,0.0003065232,0.001106698,0.002225993,0.000370003,0.00145188,0.001023028,0.001500915,0.0007251011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001342076,"about_ca_system_score_gemma":0.001660931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006874366,"about_ca_topic_score_gemma":0.005169766,"domain_scores_codex":[0.9953935,0.002646179,0.000414261,0.000681274,0.0007306687,0.0001341088],"domain_scores_gemma":[0.9847061,0.009843154,0.001465234,0.001270459,0.002503262,0.0002118206],"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":[0.0004822248,0.001825134,0.4388847,0.0004348494,0.001295282,0.0002537369,0.000251671,0.2495005,0.001549033,0.002153432,0.004668214,0.2987013],"study_design_scores_gemma":[0.00003510119,0.000333561,0.03586034,0.0001447478,0.0001007446,0.0001312434,0.00007631061,0.9586188,0.001186669,0.002009697,0.001477487,0.00002529783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6260191,0.004287676,0.3507864,0.002909261,0.0002455442,0.001265068,0.008053666,0.001427088,0.005006213],"genre_scores_gemma":[0.8756968,0.0008406828,0.1164308,0.0003184722,0.0001601724,0.0005171227,0.005205234,0.00002802629,0.0008027997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01483644,"threshold_uncertainty_score":0.07846355,"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."}}