{"id":"W4288442515","doi":"10.1371/journal.pone.0271723","title":"An evolutionary machine learning algorithm for cardiovascular disease risk prediction","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Framingham Risk Score; Receiver operating characteristic; Artificial intelligence; Machine learning; Cohort; Artificial neural network; F1 score; Population; Random forest; Framingham Heart Study; Medicine; Percentile; Convolutional neural network; Algorithm; Computer science; Disease; Statistics; Internal medicine; Mathematics","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":[0.001409143,0.0007861688,0.0008877919,0.001122839,0.0004383307,0.0006199107,0.0009105174,0.00103391,0.00161893],"category_scores_gemma":[0.003667191,0.0003383742,0.0007677579,0.0008790285,0.0004597906,0.0005541811,0.0006512512,0.0009688358,0.0003796468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007702697,"about_ca_system_score_gemma":0.001070786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006620891,"about_ca_topic_score_gemma":0.003406113,"domain_scores_codex":[0.9995133,0.0001997373,0.00003642555,0.0001117019,0.00009589726,0.00004291659],"domain_scores_gemma":[0.9988247,0.0008003939,0.00007484318,0.00004712928,0.0002275256,0.00002533109],"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":[0.00003869881,0.00006605502,0.004323971,0.00005027464,0.0001079919,0.00007532923,0.00005590962,0.8415887,0.0007052555,0.004733223,0.001110503,0.1471441],"study_design_scores_gemma":[0.000006962083,0.00001916523,0.0002182105,0.000007117607,0.000008250123,0.00001618863,0.00000392418,0.9975566,0.0001174147,0.001691857,0.0003513569,0.000002960692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04233133,0.0007235106,0.952735,0.000438355,0.0001047493,0.00009292643,0.000104721,0.0004949015,0.002974453],"genre_scores_gemma":[0.5284988,0.0005174259,0.4660073,0.0003216529,0.0001111874,0.0004768124,0.0003290329,0.00008389978,0.003653901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006620891,"threshold_uncertainty_score":0.0131647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1457417547920757,"score_gpt":0.3784191505267768,"score_spread":0.2326773957347012,"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."}}