{"id":"W4283008433","doi":"10.2196/36958","title":"Predicting Risk of Hypoglycemia in Patients With Type 2 Diabetes by Electronic Health Record–Based Machine Learning: Development and Validation","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Diabetes Management and Research","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hypoglycemia; Medicine; Type 2 diabetes; Machine learning; Artificial intelligence; Diabetes mellitus; Receiver operating characteristic; Computer science; Type 1 diabetes; Health records; Pediatrics; Intensive care medicine; Health care; Endocrinology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008411391,0.0005111934,0.0004464047,0.001677016,0.0002375216,0.0009009538,0.0007061922,0.0006152766,0.0005259527],"category_scores_gemma":[0.01813855,0.0002217086,0.000793922,0.001317512,0.0002206747,0.0008229034,0.0005704065,0.0007176292,0.0001999736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007249508,"about_ca_system_score_gemma":0.001254329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006278712,"about_ca_topic_score_gemma":0.005409047,"domain_scores_codex":[0.9970854,0.001677616,0.0003191866,0.000302809,0.0004957912,0.0001192235],"domain_scores_gemma":[0.9894979,0.006500725,0.001303506,0.0008262896,0.00167481,0.0001967228],"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":[0.0005925148,0.0008538195,0.9138513,0.0001022752,0.0004017763,0.00007852805,0.0001052762,0.01692131,0.0003552384,0.0001579037,0.0007915593,0.06578847],"study_design_scores_gemma":[0.0002015251,0.001603095,0.4379941,0.0001894602,0.0003997868,0.0003486414,0.0003251166,0.5544183,0.002718156,0.0005830254,0.001174886,0.00004393905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987228,0.0005153545,0.009970078,0.0002547657,0.00003449458,0.0001808973,0.0009632401,0.0001052915,0.0007477726],"genre_scores_gemma":[0.9825131,0.0003126427,0.01511155,0.00007639758,0.00002830187,0.0001318638,0.001668886,0.000006821558,0.0001504088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008411391,"threshold_uncertainty_score":0.0444842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00771641325046277,"score_gpt":0.2593972263264506,"score_spread":0.2516808130759878,"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."}}