{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00105798,0.0001499438,0.0003759998,0.000297133,0.0007065184,0.00002379372,0.00008224791,0.00006895565,0.0001804119],"category_scores_gemma":[0.00009262142,0.0001374816,0.00004117596,0.0003350965,0.0001471057,0.0001657504,0.0002542304,0.001088823,0.000004751232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000421783,"about_ca_system_score_gemma":0.0003950139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000338124,"about_ca_topic_score_gemma":0.00000714576,"domain_scores_codex":[0.9974587,0.0003688108,0.0005017261,0.0003153792,0.0008358358,0.0005195293],"domain_scores_gemma":[0.9990584,0.0002646103,0.0001182156,0.000148974,0.0001946674,0.0002151882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003749332,0.0008153671,0.06581053,0.0004927013,0.0003575023,0.00001896377,0.03357649,0.00002045739,0.002137986,0.001014736,0.0008770208,0.8911289],"study_design_scores_gemma":[0.04974189,0.05201817,0.1906283,0.001195343,0.0002132633,0.0006054917,0.03488652,0.1238597,0.00871785,0.007496913,0.5292675,0.001369066],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949861,0.002112345,0.0005140993,0.0006725214,0.00008434259,0.0006875712,0.0001458021,0.00004096123,0.0007562961],"genre_scores_gemma":[0.9967468,0.0008209325,0.001033174,0.00008636025,0.00004941856,0.0002533345,0.0006315472,0.0000201337,0.000358348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8897598,"threshold_uncertainty_score":0.5606337,"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."}}