{"id":"W4386843074","doi":"10.18280/ria.370429","title":"Improving Cardiovascular Disease Prognosis Using Outlier Detection and Hyperparameter Optimization of Machine Learning Models","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperparameter; Machine learning; Artificial intelligence; Outlier; Anomaly detection; Computer science; Disease; Hyperparameter optimization; Medicine; Support vector machine; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.001492343,0.0002301314,0.0004264523,0.0003403043,0.0009632508,0.0000231054,0.0001776749,0.0002053877,0.0001028215],"category_scores_gemma":[0.001003071,0.0002344853,0.0002011976,0.0009756491,0.0001417484,0.0003344804,0.0002133789,0.0005976094,0.0001131386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001365814,"about_ca_system_score_gemma":0.0001243742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002541144,"about_ca_topic_score_gemma":0.0000885444,"domain_scores_codex":[0.9968933,0.0005408111,0.001009843,0.0006025746,0.0003375788,0.000615912],"domain_scores_gemma":[0.9979667,0.0005377276,0.0003292124,0.0005081915,0.0004262537,0.0002319022],"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":[0.00005624401,0.00002717794,0.01120892,0.0005899689,0.00004431387,0.000006793763,0.002704563,0.942414,0.001641311,0.0001431633,0.000002362948,0.04116122],"study_design_scores_gemma":[0.00003298407,0.00006493256,0.00006727724,0.0002965329,0.0001031289,0.000002398661,0.004535959,0.9880335,0.005796301,0.0006520752,0.000198728,0.0002161697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5096201,0.001209039,0.486977,0.0001275746,0.0004806488,0.001182488,0.00001785708,0.0002487282,0.000136594],"genre_scores_gemma":[0.9959823,0.0006119382,0.002653611,0.00003904159,0.0001486887,0.0001570925,0.00001996848,0.0000670321,0.0003203276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4863622,"threshold_uncertainty_score":0.9562032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1904753452039148,"score_gpt":0.3806853233173924,"score_spread":0.1902099781134776,"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."}}