{"id":"W3106377951","doi":"10.1002/ehf2.13073","title":"Machine Learning vs. Conventional Statistical Models for Predicting Heart Failure Readmission and Mortality","year":2020,"lang":"en","type":"article","venue":"ESC Heart Failure","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":174,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto; Ontario Ministry of Health and Long-Term Care; Heart and Stroke Foundation of Canada","keywords":"Heart failure; Medicine; Statistical learning; Intensive care medicine; Internal medicine; Cardiology; Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09698517,0.002479502,0.01016369,0.009243553,0.0004328158,0.004501067,0.00306528,0.00319699,0.001751824],"category_scores_gemma":[0.1853658,0.001084217,0.02060581,0.007113291,0.001324524,0.00474873,0.001578725,0.002563464,0.0003127524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00301842,"about_ca_system_score_gemma":0.003324935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002718546,"about_ca_topic_score_gemma":0.003880786,"domain_scores_codex":[0.8871461,0.0810343,0.01759184,0.004940278,0.008816686,0.0004708924],"domain_scores_gemma":[0.6994637,0.2721572,0.01998244,0.002787081,0.005259532,0.0003500391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"observational","study_design_scores_codex":[0.003783188,0.0001448771,0.02479359,0.5720849,0.1693881,0.000156776,0.0003573601,0.007511946,0.0002962706,0.004048557,0.001971287,0.2154632],"study_design_scores_gemma":[0.004483357,0.006316273,0.03777993,0.4114244,0.4601313,0.0009463911,0.0006208887,0.03609294,0.001309583,0.02198153,0.01846869,0.0004446113],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.006291199,0.9825993,0.007638443,0.00150883,0.0004876429,0.0005249747,0.0004201835,0.00004036919,0.0004890664],"genre_scores_gemma":[0.3454546,0.6063626,0.03720849,0.003428233,0.003064489,0.003313435,0.0007347508,0.00005788794,0.0003755106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09698517,"threshold_uncertainty_score":0.5129128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03372690755367373,"score_gpt":0.3004233238122977,"score_spread":0.2666964162586239,"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."}}