{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000246602,0.0001258202,0.0002662327,0.00009494343,0.00009105879,0.00001623871,0.00002759217,0.00005028351,0.0003498601],"category_scores_gemma":[0.00003520592,0.000110312,0.0009790611,0.0001522613,0.00002414726,0.00009853402,0.00001982863,0.0000717869,0.00005092855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009633607,"about_ca_system_score_gemma":0.0002164397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008217088,"about_ca_topic_score_gemma":0.000005831832,"domain_scores_codex":[0.9986946,0.00005170086,0.0002364015,0.0002841344,0.0004961878,0.0002369736],"domain_scores_gemma":[0.9990468,0.000006420988,0.00002556298,0.0004813432,0.00008367342,0.000356198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004009012,0.002449497,0.8453568,0.004672644,0.005981343,0.002338046,0.00006193951,0.01996365,0.007983726,0.003199602,0.02726144,0.07672232],"study_design_scores_gemma":[0.002872179,0.00003377571,0.9529461,0.0000557172,0.00288749,0.0001682139,0.000008521592,0.01288373,0.0001366927,0.0002989188,0.02753517,0.0001734421],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544848,0.006914073,0.02848006,0.00022366,0.0004929363,0.0008843456,0.00006058505,0.0003450131,0.008114502],"genre_scores_gemma":[0.9967321,0.00004784624,0.0007879757,0.0001613894,0.000978657,0.0000248957,0.0003474959,0.00002396679,0.0008957384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1075894,"threshold_uncertainty_score":0.4498391,"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."}}