{"id":"W2883464116","doi":"10.1093/eurheartj/ehy404","title":"Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging","year":2018,"lang":"en","type":"review","venue":"European Heart Journal","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":539,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Coronary artery disease; Machine learning; Relevance (law); Modalities; Artificial intelligence; Disease; Coronary angiography; Cardiac imaging; Cardiology; Internal medicine; Computer science; Myocardial infarction","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":[0.002265777,0.0006430461,0.001067814,0.002924712,0.0003113734,0.001499311,0.001008373,0.001908045,0.002647711],"category_scores_gemma":[0.00427979,0.0002686103,0.0005876432,0.002527305,0.001373844,0.001685183,0.0009532379,0.002407831,0.001196652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007887689,"about_ca_system_score_gemma":0.00168841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001167739,"about_ca_topic_score_gemma":0.001481991,"domain_scores_codex":[0.9993495,0.0001934684,0.000100865,0.0001096996,0.0002157078,0.00003058471],"domain_scores_gemma":[0.9951802,0.00389394,0.0002099498,0.00008771191,0.0005400037,0.00008806823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003375365,0.00005322972,0.0007143941,0.0133378,0.0000965205,0.0002586217,0.0001871545,0.0009277366,0.0004966079,0.01662213,0.01466274,0.9526093],"study_design_scores_gemma":[0.00001286002,0.0001308539,0.003701446,0.01398854,0.0001553766,0.003698329,0.0002471715,0.0008906685,0.0006874372,0.02296973,0.9534512,0.00006641754],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001400102,0.9968072,0.000662099,0.00111124,0.000196888,0.000004959465,0.00001348839,0.00000727213,0.001056784],"genre_scores_gemma":[0.001867667,0.9960819,0.0007733091,0.0004406913,0.0005602909,0.000007994537,0.00002017889,0.000002704681,0.000245321],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002924712,"threshold_uncertainty_score":0.01198274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05741497653989861,"score_gpt":0.3820033057549449,"score_spread":0.3245883292150463,"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."}}