{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01177573,0.001904689,0.001775882,0.001735873,0.0005145018,0.001276416,0.001852403,0.001486319,0.001445196],"category_scores_gemma":[0.01996419,0.0005471652,0.002844105,0.001055997,0.0003973574,0.00162569,0.001386527,0.001899567,0.000416218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001702073,"about_ca_system_score_gemma":0.001529287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01413899,"about_ca_topic_score_gemma":0.005946808,"domain_scores_codex":[0.9964491,0.002236551,0.0003066616,0.0005384507,0.0002671727,0.000202039],"domain_scores_gemma":[0.975852,0.0199457,0.001018087,0.001085197,0.001590719,0.0005083184],"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":[0.01478251,0.006117515,0.3537509,0.0005894748,0.00487569,0.0001723983,0.000447296,0.4586182,0.0006034337,0.001228893,0.005257671,0.153556],"study_design_scores_gemma":[0.0003063692,0.001603502,0.02229052,0.00005481562,0.0003000726,0.00004822234,0.0000981043,0.9741256,0.0002359857,0.0006728341,0.0002296563,0.00003421485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9680107,0.002211838,0.02485741,0.0009311704,0.0001776148,0.0003996989,0.001591585,0.0003639036,0.001456124],"genre_scores_gemma":[0.9842528,0.0006042627,0.0119339,0.0001932303,0.00008239187,0.0002751741,0.002132141,0.000045191,0.0004810348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01413899,"threshold_uncertainty_score":0,"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."}}