{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009470849,0.0001146777,0.0001972307,0.0001484051,0.001569219,0.000007419909,0.00008565643,0.0001092469,0.0004345608],"category_scores_gemma":[0.0005991262,0.000107617,0.00006326153,0.0002965112,0.00001570555,0.000139689,0.0001060238,0.0009452372,0.00003997566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006095539,"about_ca_system_score_gemma":0.0002078436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003020936,"about_ca_topic_score_gemma":0.004458294,"domain_scores_codex":[0.9975073,0.000888638,0.0005756869,0.0002861712,0.0003724376,0.0003698215],"domain_scores_gemma":[0.9987394,0.0004943399,0.000351593,0.0001723723,0.0001684208,0.00007388655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000805388,0.00001245895,0.9724214,0.00004048126,0.000007405876,2.736648e-7,0.002254948,0.02040651,0.000002368693,0.00008667759,0.0009567139,0.003005368],"study_design_scores_gemma":[0.001078757,0.0009503497,0.8793225,0.00005657141,0.00001882626,3.791134e-7,0.004459828,0.0316074,0.000005962737,0.0004261826,0.081939,0.0001343028],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944903,0.00005829407,0.0004251629,0.001163861,0.0008263954,0.002629813,0.0002146964,0.0001034535,0.00008804958],"genre_scores_gemma":[0.9979091,0.00000582334,0.0006338199,0.00008057057,0.0006251902,0.0001784667,0.0003674205,0.00003405913,0.0001655371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09309895,"threshold_uncertainty_score":0.9997306,"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."}}