{"id":"W2121509234","doi":"10.1109/mwscas.2003.1562297","title":"Cardiovascular disease prediction using support vector machines","year":2006,"lang":"en","type":"article","venue":"","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Thomas Hospital","funders":"","keywords":"Support vector machine; Computer science; Disease; Artificial intelligence; Machine learning; Medicine; Internal medicine","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":[0.00159868,0.0008944512,0.001075345,0.001960227,0.0002535649,0.0009565486,0.0008385262,0.0009981452,0.002231668],"category_scores_gemma":[0.008479484,0.000257116,0.0006216926,0.00109273,0.0002082434,0.0008519012,0.0005070166,0.001228683,0.001247529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002745861,"about_ca_system_score_gemma":0.00052142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002757696,"about_ca_topic_score_gemma":0.001438692,"domain_scores_codex":[0.9989255,0.0003697944,0.0001103211,0.0001777017,0.0003250047,0.0000916751],"domain_scores_gemma":[0.9966556,0.00219203,0.0002668648,0.0001461861,0.0006235358,0.0001158065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005058332,0.0003781073,0.02265581,0.0002402051,0.0002297176,0.0002930916,0.00006258779,0.1636635,0.002612357,0.00221645,0.01331762,0.7938247],"study_design_scores_gemma":[0.00003478968,0.000144967,0.002878101,0.00003018726,0.00002127628,0.00008712593,0.00001882607,0.9904274,0.001129042,0.003720079,0.001485452,0.00002275327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09741563,0.002833855,0.8891515,0.001140048,0.0005251541,0.0002204319,0.001585595,0.00450986,0.002617893],"genre_scores_gemma":[0.7795244,0.001181222,0.213377,0.0001948208,0.0004787134,0.0003084022,0.002124678,0.00005212891,0.002758487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002757696,"threshold_uncertainty_score":0.00845474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01499818704823892,"score_gpt":0.2651450050865986,"score_spread":0.2501468180383596,"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."}}