{"id":"W4362712651","doi":"10.2196/41725","title":"Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Heart Failure Treatment and Management","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Machine learning; Ensemble learning; Artificial intelligence; Computer science; Ensemble forecasting; Logistic regression; Retrospective cohort study; Principal component analysis; Data mining; Statistics; Medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.003571873,0.0006916028,0.0006913524,0.003230121,0.001751595,0.001144414,0.001590763,0.0005244929,0.001803628],"category_scores_gemma":[0.01522422,0.0004525159,0.001440013,0.00625251,0.0006434966,0.0004950086,0.0009208267,0.001360638,0.0003138001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01598449,"about_ca_system_score_gemma":0.0306139,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9505831,"about_ca_topic_score_gemma":0.9433188,"domain_scores_codex":[0.9966341,0.0002940347,0.0003464206,0.0004576308,0.001748304,0.0005195962],"domain_scores_gemma":[0.9865626,0.001848955,0.00291984,0.001309379,0.006402076,0.0009572054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000753979,0.0000381199,0.9934517,0.00005566655,0.0001246012,0.00008439567,0.0001204965,0.000405832,0.00002596833,0.0001193859,0.001613219,0.003885233],"study_design_scores_gemma":[0.00001351832,0.00005873946,0.9929653,0.00009429957,0.0002001419,0.0002622087,0.0005277802,0.003056816,0.0001166627,0.0001038927,0.002569076,0.0000314365],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9529818,0.001975534,0.001912905,0.0006328729,0.00003642161,0.0002297955,0.03980363,0.00006888713,0.002358037],"genre_scores_gemma":[0.9767773,0.001115137,0.001812965,0.0001667104,0.00002924415,0.0001280884,0.01929943,0.00002167309,0.0006495405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0494169,"threshold_uncertainty_score":0.1159762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07834368861288107,"score_gpt":0.3532538790100524,"score_spread":0.2749101903971714,"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."}}