{"id":"W3094280923","doi":"10.18280/ria.340401","title":"Stratification of Cardiovascular Diseases Using Deep Learning","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ant colony optimization algorithms; Artificial intelligence; Artificial neural network; Backpropagation; Coronary artery disease; Computer science; Pericardial effusion; Magnetic resonance imaging; Fuzzy logic; Deep learning; Machine learning; Artificial bee colony algorithm; Medicine; Pattern recognition (psychology); Cardiology; Radiology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005250458,0.0004764638,0.0004334331,0.001404815,0.0002240482,0.0006170233,0.0005589866,0.0006019303,0.001646757],"category_scores_gemma":[0.001199915,0.0001708896,0.0005714028,0.000556717,0.000150446,0.0005103669,0.0006433725,0.0006361721,0.0004448712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004935733,"about_ca_system_score_gemma":0.0006368673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005448213,"about_ca_topic_score_gemma":0.006840888,"domain_scores_codex":[0.9998031,0.00004080993,0.0000192881,0.00004967845,0.00004451788,0.00004247043],"domain_scores_gemma":[0.9996899,0.00009946771,0.000039778,0.00002258996,0.0001064635,0.00004177394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005801512,0.0007478904,0.1472802,0.0001644539,0.0003831144,0.00039943,0.0001237142,0.2175043,0.00752429,0.002949949,0.01001553,0.6123269],"study_design_scores_gemma":[0.00001326089,0.0001036464,0.01208841,0.00003221013,0.00004314736,0.0001153516,0.00003754275,0.9813804,0.001438359,0.003559273,0.001171649,0.00001668256],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5334224,0.003993636,0.4480305,0.002253901,0.0002484566,0.0002531473,0.00281712,0.001225602,0.007755255],"genre_scores_gemma":[0.9628013,0.0005687313,0.0313221,0.0002239429,0.00007732991,0.00007707957,0.002000977,0.00001842019,0.002910169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005448213,"threshold_uncertainty_score":0.01083302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2092476450924102,"score_gpt":0.4257127653293898,"score_spread":0.2164651202369796,"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."}}