{"id":"W3202078810","doi":"10.2196/26407","title":"Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Pharmaceutical University; National Natural Science Foundation of China","keywords":"Conditional random field; Pharmacovigilance; Named-entity recognition; Computer science; Artificial intelligence; Natural language processing; Drug reaction; Recall; Adverse drug reaction; Medicine; Machine learning; Adverse effect; Drug; Pharmacology; Engineering; Psychology; Cognitive psychology","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":[0.001108452,0.0009534886,0.0006552216,0.0007351958,0.0003436628,0.0005000134,0.001290521,0.0008562078,0.001341866],"category_scores_gemma":[0.002280966,0.0003542474,0.0009336465,0.0007534964,0.0003744219,0.001857494,0.000766169,0.001253924,0.000634383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008624048,"about_ca_system_score_gemma":0.001316503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01665006,"about_ca_topic_score_gemma":0.01640441,"domain_scores_codex":[0.9995217,0.0001032025,0.00004116763,0.0002173133,0.00005650772,0.00006005047],"domain_scores_gemma":[0.9991394,0.0004536802,0.00009291284,0.00009650605,0.000178282,0.00003910887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006547255,0.0004002003,0.008049357,0.0003517167,0.0002368794,0.0006463665,0.0003587128,0.4745489,0.01117758,0.00659886,0.01043199,0.4865447],"study_design_scores_gemma":[0.0000110772,0.00004662796,0.0007988916,0.00000823714,0.00003313659,0.00005078577,0.00001687734,0.9953544,0.001619888,0.001368295,0.0006761756,0.00001563497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2677929,0.002481614,0.7130421,0.001555743,0.0004457069,0.0002692491,0.002463624,0.006569183,0.00537987],"genre_scores_gemma":[0.8952323,0.0007331762,0.09337436,0.0003276166,0.0001063816,0.0001804467,0.003733438,0.0001024728,0.006209718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01665006,"threshold_uncertainty_score":0.03310627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03818304628788474,"score_gpt":0.4215045395778254,"score_spread":0.3833214932899406,"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."}}