{"id":"W2980913398","doi":"10.2196/14340","title":"Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute","keywords":"Artificial intelligence; F1 score; Computer science; Machine learning; Convolutional neural network; Deep learning; Hypoglycemia; Support vector machine; Recurrent neural network; Population; Medicine; Precision and recall; Artificial neural network; Electronic health record; Sentence; Diabetes mellitus; Natural language processing; Health care","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001030992,0.000151885,0.0004203108,0.00009106324,0.0000731425,0.00001744447,0.001104276,0.0001142921,0.00006325246],"category_scores_gemma":[0.0007292263,0.0001017261,0.00007689749,0.0004797652,0.00005001871,0.0002831552,0.0003323835,0.0005724636,0.00003983467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001310938,"about_ca_system_score_gemma":0.0004291369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002387679,"about_ca_topic_score_gemma":0.00009292223,"domain_scores_codex":[0.996556,0.0003818801,0.001251895,0.0001444133,0.00126359,0.0004021766],"domain_scores_gemma":[0.9968807,0.001275493,0.0008268748,0.0007262124,0.0001362875,0.000154467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006229778,0.0002735239,0.8046739,0.0002670476,0.00003315326,5.913743e-8,0.01108307,0.000015211,0.000002556896,0.00003289989,0.00009194266,0.1835204],"study_design_scores_gemma":[0.001192249,0.002734149,0.5617033,0.0002935524,0.00000826084,5.942932e-7,0.0007133961,0.4323089,0.00004641436,0.0005376662,0.0003210384,0.0001405295],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915044,0.00004232069,0.006089038,0.0008951022,0.0003386404,0.001018593,0.000004597331,0.00008584933,0.0000214505],"genre_scores_gemma":[0.9974129,0.00000975844,0.001430068,0.001057752,0.00003048497,0.00003809333,0.000009393433,0.000008718843,0.000002878673],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4322937,"threshold_uncertainty_score":0.414827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01279401174519381,"score_gpt":0.3156830443010173,"score_spread":0.3028890325558236,"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."}}