{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001447445,0.0001312906,0.0002699717,0.0001105709,0.004446888,0.000005405747,0.0001991023,0.00008015765,0.0008873772],"category_scores_gemma":[0.0006048183,0.0001474811,0.000207952,0.0002150459,0.00003722171,0.0001536044,0.0001395307,0.001197402,0.0000928604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005124746,"about_ca_system_score_gemma":0.0003138944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002328288,"about_ca_topic_score_gemma":0.00004513318,"domain_scores_codex":[0.996142,0.001724642,0.0004770748,0.0004471341,0.0007284415,0.0004806705],"domain_scores_gemma":[0.9982967,0.0003983976,0.0001717775,0.000537817,0.0002942874,0.0003010672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000458494,0.002446171,0.8877608,0.000765874,0.001380026,0.0000205085,0.004371667,0.02473062,0.0002034697,0.000293787,0.001133927,0.07643466],"study_design_scores_gemma":[0.0002658935,0.000476017,0.008575104,0.00008494649,0.0005615648,4.884403e-7,0.002994956,0.9751312,0.00003786924,0.001832021,0.009858299,0.0001816676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8129587,0.01924639,0.1353949,0.001836041,0.002350229,0.01173484,0.01412583,0.002009301,0.0003437082],"genre_scores_gemma":[0.9550717,0.0007212376,0.02949993,0.0004232692,0.002441841,0.007654473,0.002389572,0.000137307,0.001660611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9504005,"threshold_uncertainty_score":0.9968492,"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."}}