{"id":"W4220799018","doi":"10.1016/j.artmed.2022.102282","title":"Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Topic Modeling","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Science Foundation of Hunan Province","keywords":"Computer science; Conditional random field; Artificial intelligence; Named-entity recognition; Natural language processing; Benchmark (surveying); Task (project management); Feature (linguistics); Deep learning; Embedding; Character (mathematics); Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002847797,0.0002005119,0.0003619058,0.0003679252,0.0002488583,0.00003988723,0.0008798089,0.00008824854,0.0003555616],"category_scores_gemma":[0.0007101932,0.000183573,0.0001102138,0.001186096,0.0001170295,0.0003253985,0.0002850909,0.0006755343,0.0001058261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001642143,"about_ca_system_score_gemma":0.0001080944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008838691,"about_ca_topic_score_gemma":0.0007448012,"domain_scores_codex":[0.9963716,0.0006009244,0.001229605,0.0007488346,0.0006639153,0.0003850884],"domain_scores_gemma":[0.9983951,0.0004308079,0.0002434056,0.0006490028,0.0001399871,0.0001416783],"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":[0.0000755451,0.001463881,0.0476115,0.0000313603,0.00002111129,0.0001168781,0.001881808,0.002364013,0.002554929,0.001583194,0.0001312033,0.9421646],"study_design_scores_gemma":[0.000224088,0.0002930922,0.005294456,0.00003828655,0.00001146272,0.00001093838,0.0002307817,0.9742455,0.0002838582,0.01884597,0.0003157126,0.0002058267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3125523,0.00009158115,0.6818986,0.002699009,0.002205877,0.0003084083,0.000001812505,0.0001733364,0.00006905646],"genre_scores_gemma":[0.9425344,0.00002843665,0.05584922,0.001071495,0.0003687632,0.00008436935,0.00002537475,0.0000131369,0.00002476818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9718815,"threshold_uncertainty_score":0.7485889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1429093938694033,"score_gpt":0.4090009094470708,"score_spread":0.2660915155776675,"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."}}