{"id":"W4206222922","doi":"10.1109/tim.2021.3139693","title":"Ensemble Machine Learning and Its Validation for Prediction of Coronary Artery Disease and Acute Coronary Syndrome Using Focused Carotid Ultrasound","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"CAD; Coronary artery disease; Acute coronary syndrome; Machine learning; Medicine; Ensemble learning; Test set; Feature (linguistics); Artificial intelligence; Ultrasound; Internal medicine; Cardiology; Computer science; Radiology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002427123,0.0001260121,0.0002049374,0.0001108366,0.0002182858,0.00001966233,0.00000951037,0.00004806081,0.0000230324],"category_scores_gemma":[0.00001675089,0.0001355468,0.00009642189,0.00008614396,0.00002808003,0.0001660112,0.000001221824,0.00008921488,3.646398e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001121421,"about_ca_system_score_gemma":0.0001553404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009664996,"about_ca_topic_score_gemma":0.000007426378,"domain_scores_codex":[0.9988844,0.00009549576,0.0002749717,0.0002627377,0.0003561396,0.0001262199],"domain_scores_gemma":[0.9993824,0.00003087888,0.00007904604,0.00008433918,0.0001933116,0.000229978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004594259,0.0009999672,0.04111647,0.002967511,0.002231102,0.00006198297,0.0006511067,0.001181101,0.8970162,0.00004175047,0.00001101887,0.04912753],"study_design_scores_gemma":[0.02421579,0.001588752,0.7165891,0.001117943,0.007594562,0.002977635,0.0008574267,0.01101682,0.2333491,0.0001687832,0.00008092038,0.0004431829],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9167107,0.0009545247,0.08087116,0.00009921015,0.0001891265,0.0009411161,0.0001986696,0.00002788519,0.000007661904],"genre_scores_gemma":[0.9975161,0.001325784,0.0007712057,0.00005755876,0.00001376279,0.00009577673,0.0001681542,0.00001564415,0.00003603706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6754726,"threshold_uncertainty_score":0.5527436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04645299799377235,"score_gpt":0.2837729343830347,"score_spread":0.2373199363892624,"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."}}