{"id":"W4366083921","doi":"10.2196/41775","title":"Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ejection fraction; Heart failure; Medicine; Logistic regression; Random forest; Machine learning; Artificial intelligence; Gradient boosting; Artificial neural network; Internal medicine; Algorithm; Diagnosis code; Emergency medicine; Cardiology; Computer science; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001010314,0.0002000159,0.0004770847,0.0007166591,0.0004636047,0.00002936583,0.00005437302,0.00007977791,0.0000123855],"category_scores_gemma":[0.0001239418,0.0001433377,0.00004459858,0.001107085,0.00005038058,0.0004360886,0.0001081108,0.0007166077,0.00000687843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001734069,"about_ca_system_score_gemma":0.00008032232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000143265,"about_ca_topic_score_gemma":0.0000950773,"domain_scores_codex":[0.9975932,0.0002266538,0.0004008505,0.0003629083,0.0009184268,0.00049798],"domain_scores_gemma":[0.9989,0.000198749,0.0001039493,0.0001906706,0.0004365907,0.0001700283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008390745,0.001207891,0.9877446,0.0001852596,0.0001337009,0.000001773816,0.007000224,0.0009676804,0.00007517849,0.000002440927,0.001315448,0.0005266873],"study_design_scores_gemma":[0.01164515,0.01900919,0.6603079,0.001405442,0.00008483627,0.000003414602,0.07659165,0.2286538,0.0003086636,0.00001419929,0.001692287,0.0002834136],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936294,0.00001036359,0.0005887415,0.001769428,0.00003874828,0.003838681,0.000007369229,0.00009428565,0.00002298888],"genre_scores_gemma":[0.99549,0.000002499406,0.003576366,0.000004451137,0.00006191219,0.0005670564,0.0001585448,0.00003614242,0.0001030356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3274367,"threshold_uncertainty_score":0.5845142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05229179804797632,"score_gpt":0.4038787297644336,"score_spread":0.3515869317164573,"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."}}