{"id":"W1978884830","doi":"10.1016/j.jclinepi.2010.04.007","title":"Diabetics can be identified in an electronic medical record using laboratory tests and prescriptions","year":2010,"lang":"en","type":"article","venue":"Journal of Clinical Epidemiology","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Ontario; Ottawa Hospital; Toronto Western Hospital; University of Toronto; University Health Network; Statistics Canada; Institute for Clinical Evaluative Sciences","funders":"Canadian Institutes of Health Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Medicine; Medical prescription; Medical record; Electronic medical record; Diabetes mellitus; Family medicine; MEDLINE; Predictive value; Emergency medicine; Pediatrics; Internal medicine; Nursing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007900053,0.0002459837,0.0004063619,0.003702692,0.0005388231,0.001164442,0.0003518904,0.0007669583,0.00301865],"category_scores_gemma":[0.01023428,0.0001997299,0.0004578075,0.004154784,0.0002201791,0.001014889,0.000725146,0.0007054076,0.000607422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003545156,"about_ca_system_score_gemma":0.0006338547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004011798,"about_ca_topic_score_gemma":0.007818567,"domain_scores_codex":[0.9982983,0.0003101898,0.0006126555,0.0001825321,0.0004701242,0.0001262387],"domain_scores_gemma":[0.9879501,0.005182753,0.00539603,0.000487809,0.0005606172,0.000422755],"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":[0.0002754164,0.0001238966,0.9878372,0.0001091787,0.00004887611,0.0009346335,0.0001263413,0.00004310505,0.0003640413,0.0001808716,0.001639499,0.008316983],"study_design_scores_gemma":[0.00005735139,0.0002209486,0.9821353,0.0002915734,0.0002594245,0.005336953,0.001002411,0.001312222,0.001323652,0.0007701221,0.007254519,0.00003551065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.968576,0.00258777,0.001206911,0.001782877,0.0002392552,0.0001241253,0.0134145,0.00007296733,0.01199554],"genre_scores_gemma":[0.9872876,0.001778147,0.00247808,0.0008843251,0.0002046458,0.00005690391,0.005604863,0.000008333715,0.001697044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004011798,"threshold_uncertainty_score":0.0100984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1852224660473834,"score_gpt":0.5136758622002351,"score_spread":0.3284533961528517,"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."}}