{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002274229,0.000183025,0.0003604905,0.0001395837,0.0004839307,0.000192688,0.0005897564,0.0002252374,0.00002404707],"category_scores_gemma":[0.0008091505,0.0001788098,0.00006125905,0.000573437,0.00001938218,0.000966071,0.0001918807,0.001044643,0.00001431457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006331879,"about_ca_system_score_gemma":0.001204679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001730254,"about_ca_topic_score_gemma":0.00008166749,"domain_scores_codex":[0.9960698,0.0007052391,0.001215727,0.0003107867,0.001119372,0.000579071],"domain_scores_gemma":[0.9976866,0.0004621367,0.0006361908,0.0005141814,0.0002858062,0.000415108],"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":[0.00005581036,0.001022672,0.5448213,0.001246164,0.000168392,0.00001403976,0.1169577,0.01320065,0.00001795058,0.007336342,0.0004740232,0.3146849],"study_design_scores_gemma":[0.0005896489,0.0005100777,0.03054886,0.0001739626,0.000008906295,0.00002282197,0.007808391,0.9579973,0.000001075075,0.0002600243,0.001914657,0.0001642801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4164473,0.0001323255,0.5810391,0.0008682999,0.000566421,0.0006799742,0.000001257935,0.0002091542,0.00005613625],"genre_scores_gemma":[0.9613167,0.00003433906,0.03735688,0.0006545167,0.0003198606,0.0001190336,0.000143437,0.00002204534,0.00003320852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9447966,"threshold_uncertainty_score":0.729165,"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."}}