{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003955496,0.000302828,0.0005740395,0.00007195758,0.0003027081,0.00006612546,0.00005537571,0.0001637175,0.0005329177],"category_scores_gemma":[0.0003251776,0.0002625142,0.0001570818,0.0001503642,0.00008571405,0.000214406,0.0001039528,0.0004380119,0.00004815811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005206294,"about_ca_system_score_gemma":0.0001200552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007876146,"about_ca_topic_score_gemma":0.0000358812,"domain_scores_codex":[0.9979041,0.0001125595,0.000427799,0.000627237,0.0004610794,0.0004671776],"domain_scores_gemma":[0.9987394,0.0001758342,0.00006670962,0.0002051146,0.0001130926,0.0006999038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00109538,0.0004642497,0.412859,0.001730638,0.0006163205,0.00009451809,0.000912839,0.0004439313,0.004759572,0.01296817,0.5627652,0.001290153],"study_design_scores_gemma":[0.004084475,0.001383324,0.01313552,0.0002059829,0.0003707704,0.00006525622,0.0003076099,0.09068198,0.0004228536,0.0006601545,0.8883537,0.0003283725],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2240632,0.0008054746,0.09120376,0.6754112,0.0002997511,0.005847711,0.0004896211,0.001215301,0.0006639439],"genre_scores_gemma":[0.8766562,0.00001685451,0.1187577,0.002620404,0.0004398512,0.0001104012,0.0006875381,0.00005793668,0.0006530328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6727908,"threshold_uncertainty_score":0.9999827,"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."}}