{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007893676,0.00060333,0.000585795,0.004557011,0.0005376043,0.001308419,0.0008988125,0.0007400058,0.001430971],"category_scores_gemma":[0.02801476,0.0001641502,0.0004973587,0.002508527,0.0003346427,0.0005557988,0.0009690703,0.0008102622,0.0005318421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002285487,"about_ca_system_score_gemma":0.003104041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04958726,"about_ca_topic_score_gemma":0.06643923,"domain_scores_codex":[0.9969097,0.001410456,0.0002714089,0.0004378511,0.0008399757,0.0001306081],"domain_scores_gemma":[0.9741643,0.01690127,0.002382345,0.002026184,0.004213103,0.0003127417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005642599,0.000505043,0.670282,0.0004429741,0.0004417214,0.0007241537,0.0003395934,0.07086471,0.00316051,0.002589597,0.0184116,0.2316738],"study_design_scores_gemma":[0.00009345172,0.0001614042,0.3201243,0.0002754563,0.0001714588,0.0004753761,0.000339081,0.6562146,0.005522598,0.007648365,0.008905366,0.00006861203],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7583846,0.003678055,0.1929848,0.005944408,0.000163175,0.0008128356,0.02841206,0.002449992,0.007169959],"genre_scores_gemma":[0.9258261,0.0003915682,0.06254071,0.0002556368,0.0001029069,0.0001156123,0.01030612,0.00002976918,0.0004317505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04958726,"threshold_uncertainty_score":0.09859723,"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."}}