{"id":"W4390204972","doi":"10.1016/j.jacadv.2023.100801","title":"Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source","year":2023,"lang":"en","type":"article","venue":"JACC Advances","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal; McGill University; Université de Montréal; McGill University Health Centre","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; McGill University; Heart and Stroke Foundation of Canada","keywords":"Gradient boosting; Decision tree; Machine learning; Artificial intelligence; Logistic regression; Precision and recall; Computer science; Decision tree learning; Audit; Support vector machine; Boosting (machine learning); Random forest; Heart disease; Metric (unit); Data mining; Medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009963401,0.0001771042,0.0003035154,0.0001909381,0.0009685362,0.000015927,0.0004557149,0.0001286562,0.0003051197],"category_scores_gemma":[0.004963055,0.0001641725,0.0000512648,0.0005532605,0.00006028788,0.0007397635,0.0004819556,0.0006638825,0.0008172421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001228983,"about_ca_system_score_gemma":0.000350437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007587941,"about_ca_topic_score_gemma":0.01485959,"domain_scores_codex":[0.9973511,0.0002726566,0.0007252872,0.0004614593,0.000250986,0.0009385822],"domain_scores_gemma":[0.9955119,0.003373612,0.000186048,0.0005497938,0.0001222383,0.0002563625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001456949,0.00003700383,0.9796991,0.0004442335,0.00000549807,0.000006289264,0.002040036,0.0004250176,0.000003098342,0.0002787095,0.005323226,0.01159204],"study_design_scores_gemma":[0.0003933086,0.00006744556,0.05817047,0.0002805468,0.00001316081,1.961554e-7,0.01031551,0.07576218,0.00001239413,0.003678363,0.8510584,0.0002479781],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.968461,0.005418631,0.001610897,0.01344166,0.003064032,0.004371355,0.002259095,0.0009016802,0.0004716385],"genre_scores_gemma":[0.989022,0.001636929,0.000506399,0.00170897,0.0005621943,0.001786528,0.001505184,0.0000604108,0.003211425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9215287,"threshold_uncertainty_score":0.9999607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2012801566200996,"score_gpt":0.5183804850484445,"score_spread":0.3171003284283449,"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."}}