{"id":"W4387950320","doi":"10.1371/journal.pdig.0000354","title":"Artificial intelligence with temporal features outperforms machine learning in predicting diabetes","year":2023,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Overfitting; Machine learning; Deep learning; Computer science; Diabetes mellitus; Predictive modelling; Body mass index; Medicine; Artificial neural network; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003688921,0.000800397,0.0005872073,0.0008134921,0.0003514849,0.001651156,0.0005197912,0.0009565796,0.001281322],"category_scores_gemma":[0.009460191,0.0001687868,0.0005830425,0.0008100133,0.0003898953,0.001748032,0.0007946744,0.001576044,0.0004579149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001045367,"about_ca_system_score_gemma":0.001310098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0213887,"about_ca_topic_score_gemma":0.01637238,"domain_scores_codex":[0.9989762,0.000369792,0.00007775449,0.0002110009,0.0002647206,0.0001005101],"domain_scores_gemma":[0.995755,0.00313539,0.0002133647,0.0003321405,0.0004147005,0.000149533],"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":[0.0008789419,0.0006468815,0.07520431,0.0002692484,0.0004011155,0.0001863646,0.0001405323,0.5665951,0.001356087,0.007340401,0.007969974,0.339011],"study_design_scores_gemma":[0.00001354332,0.0001742952,0.007317826,0.00005959617,0.00004464568,0.00002803967,0.00004582681,0.9855142,0.0005364057,0.005195905,0.001054053,0.0000156765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.835234,0.01641496,0.1052434,0.009041933,0.001107219,0.0001323784,0.001663981,0.001584667,0.02957746],"genre_scores_gemma":[0.9826249,0.001218901,0.01343655,0.0003566782,0.0001464263,0.00001323631,0.0007161049,0.00002271623,0.001464484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0213887,"threshold_uncertainty_score":0.04252845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1350635191125512,"score_gpt":0.4161526133799204,"score_spread":0.2810890942673693,"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."}}