{"id":"W4403759266","doi":"10.1371/journal.pdig.0000636","title":"Machine Learning For Risk Prediction After Heart Failure Emergency Department Visit or Hospital Admission Using Administrative Health Data","year":2024,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Canadian VIGOUR Centre; Northern Alberta Institute of Technology; Libin Cardiovascular Institute of Alberta","funders":"Servier; University of Alberta; Servier Canada","keywords":"Emergency department; Heart failure; Medical emergency; Emergency medicine; Medicine; Hospital admission; Nursing; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003769179,0.0003246958,0.0004992037,0.0001604058,0.0003220631,0.0001372024,0.00009601755,0.00007488657,0.0002397002],"category_scores_gemma":[0.0001512933,0.0002374891,0.0001332488,0.0002778926,0.00002559697,0.0006904861,0.0001448742,0.0003130455,0.00005881194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005041061,"about_ca_system_score_gemma":0.001000821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001050329,"about_ca_topic_score_gemma":0.0001451978,"domain_scores_codex":[0.9973865,0.00008231127,0.0006772882,0.0007928156,0.0004404355,0.0006206245],"domain_scores_gemma":[0.9985973,0.00007853303,0.000143817,0.000473195,0.00006195073,0.0006451727],"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":[0.005812522,0.009159745,0.3252297,0.01827077,0.003266966,0.0004330044,0.005954579,0.00007596044,0.00004726192,0.0003516936,0.4372158,0.194182],"study_design_scores_gemma":[0.002222889,0.01750609,0.008203546,0.002341009,0.0003729589,0.00008061208,0.0007841327,0.05630158,0.0000447672,0.000130545,0.9115531,0.0004587783],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6646478,0.03274681,0.03929636,0.205372,0.003339287,0.02894903,0.02150261,0.003423868,0.0007222187],"genre_scores_gemma":[0.9834782,0.000862747,0.008409673,0.0003573849,0.0004899153,0.0001861963,0.004395175,0.00008325995,0.001737377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4743373,"threshold_uncertainty_score":0.9684525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07152594225822775,"score_gpt":0.3726111591996459,"score_spread":0.3010852169414181,"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."}}