{"id":"W2981121978","doi":"10.1186/s12902-019-0436-6","title":"Predictive models for diabetes mellitus using machine learning techniques","year":2019,"lang":"en","type":"article","venue":"BMC Endocrine Disorders","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":294,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Fields Institute for Research in Mathematical Sciences; University of Toronto; York University","funders":"Division of Mathematical Sciences; Natural Sciences and Engineering Research Council of Canada; Fields Institute for Research in Mathematical Sciences","keywords":"Logistic regression; Medicine; Decision tree; Diabetes mellitus; Random forest; Receiver operating characteristic; Machine learning; Artificial intelligence; Sensitivity (control systems); Body mass index; Predictive modelling; Decision tree model; Internal medicine; Statistics; Computer science; Mathematics; Endocrinology; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004587173,0.0002677011,0.0004066764,0.0001761322,0.0007142198,0.00001159388,0.000256896,0.0001443686,0.0002995044],"category_scores_gemma":[0.0004870063,0.0002558508,0.0001537563,0.0002287871,0.00007061512,0.0003056195,0.0001565655,0.0007059788,0.0001099921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002106998,"about_ca_system_score_gemma":0.000262497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002557543,"about_ca_topic_score_gemma":0.002684044,"domain_scores_codex":[0.9971614,0.0004054016,0.0006829254,0.0005023279,0.0002422797,0.001005627],"domain_scores_gemma":[0.9976258,0.001310407,0.0002813207,0.0003746914,0.0002720848,0.0001357273],"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":[0.0002224539,0.00008366766,0.9683661,0.001366414,0.00003974498,4.807495e-7,0.002288358,0.01642901,0.001109332,0.004627656,0.0001491361,0.005317687],"study_design_scores_gemma":[0.0004180308,0.0004125203,0.0005101168,0.0003868532,0.00004000878,4.663211e-7,0.006391782,0.944346,0.003682071,0.02501017,0.01839054,0.0004114465],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8996397,0.00216116,0.08235322,0.0007888646,0.001208161,0.00750055,0.0001520982,0.0009694169,0.005226779],"genre_scores_gemma":[0.9843715,0.0003432171,0.01235515,0.0003975135,0.000239105,0.0008718825,0.00009481473,0.0001116889,0.001215075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9678559,"threshold_uncertainty_score":0.9999894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09883913857235904,"score_gpt":0.4360000462798314,"score_spread":0.3371609077074723,"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."}}