{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005824699,0.0007010712,0.0007043546,0.00151504,0.0003466738,0.0007696053,0.0006978419,0.0006312257,0.0005310102],"category_scores_gemma":[0.01547392,0.0002288802,0.0007582505,0.0009514023,0.0003135319,0.0004622851,0.0007327728,0.0009567796,0.0002223727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001289555,"about_ca_system_score_gemma":0.001870214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0284306,"about_ca_topic_score_gemma":0.02351158,"domain_scores_codex":[0.998328,0.001034188,0.0000956199,0.0002301371,0.0001724091,0.0001395916],"domain_scores_gemma":[0.9942225,0.004144541,0.0005549395,0.0002646921,0.0006364458,0.0001769491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008730048,0.000669848,0.7217827,0.0001859746,0.001003494,0.000199703,0.0001712192,0.1646232,0.001178305,0.0006302561,0.002694414,0.1059879],"study_design_scores_gemma":[0.00005649288,0.0003380505,0.1251159,0.00007273342,0.0001294934,0.00008987643,0.00009734237,0.8708457,0.0009230876,0.001763906,0.0005402825,0.00002714658],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9361127,0.001566005,0.05805776,0.001132815,0.00006962133,0.000194599,0.001562795,0.0003732282,0.0009304399],"genre_scores_gemma":[0.9873521,0.0001273969,0.01135044,0.00007805682,0.00002200831,0.0000524936,0.0008135523,0.000007542857,0.0001963518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0284306,"threshold_uncertainty_score":0.05653024,"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."}}