{"id":"W3168290506","doi":"10.2196/27527","title":"Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study","year":2021,"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":"U.S. National Library of Medicine; National Institute on Drug Abuse; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Institutes of Health","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Deep learning; F1 score; Machine learning; Natural language processing; Macro; Encoder; Test set; Data set; Relation (database); Artificial neural network; Data mining","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":[0.006212187,0.001260657,0.0007699376,0.00193848,0.0005557508,0.00110362,0.001476486,0.001280342,0.001148088],"category_scores_gemma":[0.01818269,0.000392708,0.001025428,0.001793143,0.0006703911,0.002211638,0.001373324,0.00241263,0.0005716335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002377221,"about_ca_system_score_gemma":0.001428633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01743931,"about_ca_topic_score_gemma":0.01704099,"domain_scores_codex":[0.9968559,0.001329661,0.0003285904,0.0006546872,0.0005491058,0.0002820138],"domain_scores_gemma":[0.9851714,0.0104068,0.00107191,0.001287403,0.001647203,0.0004153374],"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":[0.003005096,0.006905347,0.4770321,0.001150909,0.000818614,0.0009338111,0.0007778301,0.09952469,0.003393181,0.001193484,0.01744192,0.387823],"study_design_scores_gemma":[0.0001257688,0.000787129,0.06895246,0.0001246673,0.0002259285,0.0002773062,0.000440907,0.9216793,0.003648078,0.001641444,0.002052529,0.00004452781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869622,0.001677025,0.007022702,0.0006101443,0.00007991068,0.0001697155,0.002118623,0.0003385408,0.001021294],"genre_scores_gemma":[0.9852771,0.0004746198,0.007465305,0.0001332263,0.00004573599,0.00008142395,0.005927978,0.00001832606,0.0005763845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01743931,"threshold_uncertainty_score":0.0346756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06727003259079725,"score_gpt":0.4104207815372956,"score_spread":0.3431507489464983,"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."}}