{"id":"W4307831606","doi":"10.1016/j.jacasi.2022.07.007","title":"Machine Learning Risk Prediction for Incident Heart Failure in Patients With Atrial Fibrillation","year":2022,"lang":"en","type":"article","venue":"JACC Asia","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"Astellas Pharma; Bayer HealthCare; Pfizer; Japan Agency for Medical Research and Development; Boehringer Ingelheim; AstraZeneca; Bristol-Myers Squibb","keywords":"Medicine; Atrial fibrillation; Internal medicine; Framingham Risk Score; Cardiology; Ejection fraction; Heart failure; Receiver operating characteristic; Cohort; Framingham Heart Study; Prospective cohort study; Incidence (geometry); Creatinine; Mathematics; Disease","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.003981967,0.0005087434,0.00062671,0.001005964,0.0002379547,0.0007060367,0.0004684435,0.0005510119,0.000532861],"category_scores_gemma":[0.01616334,0.0001692769,0.0007543209,0.0005031305,0.0001875278,0.0004980384,0.0004713516,0.0008958892,0.0002473883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002994379,"about_ca_system_score_gemma":0.000620741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003002687,"about_ca_topic_score_gemma":0.002256519,"domain_scores_codex":[0.9988633,0.0006345556,0.00009349043,0.0001675709,0.000145111,0.00009589562],"domain_scores_gemma":[0.9936386,0.004518591,0.000705214,0.0003671867,0.0005665524,0.0002038697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003328015,0.0001969754,0.9481336,0.00001694875,0.0002065393,0.00006975713,0.00005368998,0.02900657,0.0001875629,0.0001262479,0.0007132867,0.02095608],"study_design_scores_gemma":[0.00007847032,0.0003541089,0.2962972,0.00003849216,0.0001667879,0.0002370297,0.0000851966,0.7001936,0.0004409573,0.001667488,0.0004103512,0.00003033268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815009,0.0004138965,0.01658674,0.0003975137,0.00004686782,0.00003725975,0.0004258355,0.0001185965,0.0004723615],"genre_scores_gemma":[0.9958388,0.00009216828,0.003317669,0.00004007449,0.0000340417,0.00002621693,0.000550081,0.000005540507,0.00009540059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003981967,"threshold_uncertainty_score":0.02105886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04367930242130334,"score_gpt":0.3824877125436772,"score_spread":0.3388084101223738,"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."}}