{"id":"W4229333283","doi":"10.2196/36176","title":"Machine Learning Prediction of Hypoglycemia and Hyperglycemia From Electronic Health Records: Algorithm Development and Validation","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Hyperglycemia and glycemic control in critically ill and hospitalized patients","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Decompensation; Hypoglycemia; Medicine; Health records; Medical record; Retrospective cohort study; Health care; Emergency medicine; Cohort; Algorithm; Electronic health record; Pediatrics; Machine learning; Internal medicine; Computer science; Insulin","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.008868434,0.001139018,0.001442071,0.00189992,0.000525266,0.001004374,0.001510416,0.001492057,0.001071648],"category_scores_gemma":[0.01534228,0.0004518866,0.001073911,0.001224711,0.0003641291,0.0007329296,0.0009445763,0.001769936,0.0004454738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092386,"about_ca_system_score_gemma":0.00234442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01173354,"about_ca_topic_score_gemma":0.005148789,"domain_scores_codex":[0.9979983,0.001026748,0.0002226509,0.0003714417,0.0002278296,0.0001530908],"domain_scores_gemma":[0.9882513,0.009112618,0.0005406446,0.0003910626,0.001504715,0.000199648],"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":[0.0006807547,0.001043956,0.06612,0.0001439455,0.0004636653,0.0001376111,0.00007811857,0.7478551,0.0006759651,0.0004151579,0.002169737,0.1802161],"study_design_scores_gemma":[0.00002945907,0.00006489458,0.002365092,0.00001271694,0.00002126155,0.00001798445,0.000009724571,0.9969355,0.0001975923,0.0002731252,0.00006817297,0.000004377873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6656786,0.001927457,0.3249029,0.0009581931,0.0001559681,0.0009150606,0.001719159,0.00222029,0.001522316],"genre_scores_gemma":[0.8745314,0.0003520766,0.1213412,0.0001529384,0.00006648251,0.0006032629,0.002414607,0.00003021135,0.0005076585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01173354,"threshold_uncertainty_score":0.04690135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03396521175762467,"score_gpt":0.3433595101922005,"score_spread":0.3093942984345758,"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."}}