{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004630503,0.0005767494,0.0004208933,0.001627487,0.0003997937,0.000759681,0.0008393208,0.0008689732,0.0006689739],"category_scores_gemma":[0.02157842,0.000280975,0.0005219136,0.001216272,0.0006036584,0.001280369,0.0009680613,0.0009611968,0.000453985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001050375,"about_ca_system_score_gemma":0.0007303509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01175768,"about_ca_topic_score_gemma":0.01339141,"domain_scores_codex":[0.9961662,0.001581352,0.0004908709,0.0008517792,0.0007200405,0.0001897634],"domain_scores_gemma":[0.9714758,0.02065115,0.002661374,0.001955734,0.002728816,0.0005270728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00148766,0.004893202,0.7743858,0.001437743,0.0006022346,0.001279435,0.002003508,0.01285153,0.00874356,0.0002963398,0.007698013,0.1843211],"study_design_scores_gemma":[0.0002430386,0.001099383,0.7320199,0.0002012825,0.0004449905,0.00164988,0.002301561,0.243226,0.01342775,0.0005385471,0.004742611,0.000105073],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943483,0.0005513921,0.002590087,0.0001538939,0.0000211154,0.00009277276,0.001743192,0.0001084876,0.000390727],"genre_scores_gemma":[0.9859993,0.0002996754,0.006372865,0.00008618493,0.00003376944,0.00005183873,0.006859327,0.00001304057,0.0002839798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01175768,"threshold_uncertainty_score":0.02448875,"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."}}