{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006144954,0.0002302079,0.0005835008,0.0001022219,0.0007576936,0.0000144278,0.0004112849,0.0002146341,0.001098849],"category_scores_gemma":[0.001555495,0.0002402076,0.0003264855,0.0008115337,0.0001809546,0.0002075221,0.0001315182,0.0007585622,0.0008799118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001267637,"about_ca_system_score_gemma":0.000222319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007132928,"about_ca_topic_score_gemma":0.00004239288,"domain_scores_codex":[0.9963429,0.0007027328,0.00138322,0.0005858371,0.0003963808,0.0005889091],"domain_scores_gemma":[0.9974099,0.0006172485,0.000460328,0.0006016307,0.0005764405,0.0003344404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001164614,0.000119813,0.04575631,0.001615017,0.0001652511,0.00001802912,0.02395823,0.8384056,0.009866933,0.0082946,0.0001083636,0.07157539],"study_design_scores_gemma":[0.00003387472,0.0001638812,0.0002135232,0.000279399,0.0001265096,0.000001887271,0.03234563,0.9366297,0.02023404,0.001063887,0.008616453,0.0002912032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4913313,0.00693211,0.4929871,0.001555284,0.0009355311,0.00200824,0.00003448292,0.0003786529,0.00383731],"genre_scores_gemma":[0.9975499,0.000344591,0.001103372,0.0002204298,0.0005046962,0.00005665853,0.00002402252,0.00004939932,0.0001469638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5062186,"threshold_uncertainty_score":0.999898,"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."}}