{"id":"W4388273445","doi":"10.1016/j.jcjd.2023.10.086","title":"COMPARISON OF MACHINE LEARNING AND CONVENTIONAL STATISTICAL MODELING FOR PREDICTING READMISSION FOLLOWING ACUTE HEART FAILURE HOSPITALIZATION","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Diabetes","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Public Health","funders":"","keywords":"Medicine; Heart failure; Intensive care medicine; Machine learning; Emergency medicine; Cardiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.008789111,0.001170254,0.001314277,0.001398873,0.000339338,0.001466505,0.001258735,0.001035402,0.001056839],"category_scores_gemma":[0.02263722,0.0002307526,0.001124133,0.0009746705,0.0002942297,0.001570429,0.0006926862,0.001079531,0.000274315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008980775,"about_ca_system_score_gemma":0.001408039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01091743,"about_ca_topic_score_gemma":0.005304282,"domain_scores_codex":[0.9973236,0.001700968,0.0002206846,0.0003282852,0.0003045701,0.0001218387],"domain_scores_gemma":[0.9652215,0.03145364,0.0006883622,0.0007310379,0.001604795,0.0003006611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009931781,0.002584142,0.2751186,0.0005105163,0.001980509,0.0001804794,0.0002703855,0.4678041,0.001041997,0.002773213,0.003630148,0.2341742],"study_design_scores_gemma":[0.00006342155,0.0004847033,0.01100536,0.00001978728,0.00009170965,0.00002428855,0.00006105517,0.9871051,0.0001306471,0.0008632041,0.0001384077,0.00001230296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9334527,0.002164514,0.0600863,0.0009240356,0.0002518145,0.00009044848,0.0009458651,0.0004866496,0.001597688],"genre_scores_gemma":[0.9868309,0.0004369409,0.0112177,0.00009454188,0.0001041427,0.00004317341,0.0008334676,0.00002981484,0.0004093201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01091743,"threshold_uncertainty_score":0.04648179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09181456231609039,"score_gpt":0.4492486693063741,"score_spread":0.3574341069902838,"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."}}