{"id":"W2983608826","doi":"10.18653/v1/d19-6219","title":"Recognizing UMLS Semantic Types with Deep Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Privacy Analytics (Canada); National Research Council Canada","funders":"","keywords":"Unified Medical Language System; Computer science; Deep learning; Natural language processing; Information retrieval; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001287588,0.001485101,0.000879807,0.006937186,0.000882754,0.002595255,0.001689011,0.001480726,0.004998498],"category_scores_gemma":[0.006010076,0.0009078346,0.00256453,0.003580519,0.0006532064,0.007286524,0.003047886,0.001915779,0.003854505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001697415,"about_ca_system_score_gemma":0.00176974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01366604,"about_ca_topic_score_gemma":0.03415771,"domain_scores_codex":[0.9985209,0.0002537353,0.0002057824,0.0003869275,0.0004605115,0.0001721177],"domain_scores_gemma":[0.9967493,0.001443311,0.0003000561,0.0005423889,0.0008110646,0.0001539358],"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":[0.000404345,0.0003743619,0.008260584,0.0006192404,0.0001471265,0.0005801109,0.0006328305,0.01580762,0.006958361,0.04022052,0.08719953,0.8387954],"study_design_scores_gemma":[0.0000785069,0.00007561413,0.002251141,0.0004369685,0.0001244027,0.0006279105,0.001003151,0.6782755,0.01288923,0.2046434,0.09951149,0.00008271264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0502329,0.003322399,0.869588,0.002940347,0.001007332,0.0004064197,0.01757891,0.04279004,0.01213346],"genre_scores_gemma":[0.2438825,0.001470414,0.7001455,0.0007354217,0.0001780846,0.0002968377,0.04404553,0.001153937,0.008091789],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01366604,"threshold_uncertainty_score":0.02717298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007885366663328479,"score_gpt":0.2302159884875539,"score_spread":0.2223306218242254,"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."}}