{"id":"W2900069888","doi":"10.2196/12159","title":"Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; U.S. Department of Veterans Affairs","keywords":"Conditional random field; Deep learning; Artificial intelligence; Named-entity recognition; Computer science; Machine learning; Pharmacovigilance; Information extraction; Relationship extraction; Task (project management); Multi-task learning; Natural language processing; Medicine; Adverse effect; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001522268,0.000227047,0.0003776363,0.0003980662,0.0002142939,0.000007040461,0.0003258414,0.0003075779,0.001163873],"category_scores_gemma":[0.0003575221,0.0002207878,0.0000899587,0.00050467,0.0001106366,0.0008828026,0.00005110678,0.001222407,0.0003503055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002640703,"about_ca_system_score_gemma":0.0006960452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006526532,"about_ca_topic_score_gemma":0.00003113975,"domain_scores_codex":[0.997126,0.0003542102,0.001226745,0.000136499,0.0006421965,0.0005143782],"domain_scores_gemma":[0.9976092,0.0005947847,0.0006920007,0.0002392249,0.0002019187,0.0006628547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002595946,0.001013743,0.000969453,0.0001934105,0.0002008062,0.000002634284,0.03019362,0.6525193,0.003383365,0.0002605522,0.002502302,0.3061648],"study_design_scores_gemma":[0.001997205,0.0008438328,0.0004463166,0.0001401734,0.00005222509,0.000003300533,0.001190269,0.9712591,0.006933784,0.0001858396,0.0167354,0.000212605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8633653,0.000008503132,0.1325547,0.0008378797,0.0005720172,0.001148202,0.00009567979,0.0001289329,0.001288821],"genre_scores_gemma":[0.9881808,0.00007286311,0.005119808,0.006162635,0.00007997153,0.00008112752,0.000242137,0.00001669109,0.00004401372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3187397,"threshold_uncertainty_score":0.9997492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05238572201969314,"score_gpt":0.4292452800969486,"score_spread":0.3768595580772555,"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."}}