{"id":"W2806165888","doi":"","title":"Extracting Adverse Drug Reactions using Deep Learning and Dictionary Based Approaches.","year":2017,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Drug reaction; Artificial intelligence; Dictionary learning; Natural language processing; Drug; Pharmacology; Medicine; Sparse approximation","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":[0.0003622391,0.00007371607,0.00008582243,0.00002575224,0.0006557007,0.00001997694,0.00008900384,0.00007237656,0.000005764952],"category_scores_gemma":[0.0001418691,0.00006809403,0.00002414538,0.00002374819,0.0006370947,0.000008318539,0.00007200848,0.00008648566,3.981761e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000280333,"about_ca_system_score_gemma":0.00002128941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001692895,"about_ca_topic_score_gemma":0.000003811607,"domain_scores_codex":[0.9995345,0.00005164325,0.0001147778,0.0001703909,0.00004391609,0.00008471609],"domain_scores_gemma":[0.999494,0.00008018471,0.0001468103,0.0002090947,0.00003029254,0.00003963611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004462828,0.00023738,0.03060959,0.0003131611,0.0001869691,0.000001764813,0.0008480198,0.000447108,0.1929144,0.1304113,0.00004548688,0.6435385],"study_design_scores_gemma":[0.003666499,0.0005484522,0.07203059,0.0001999857,0.000668213,0.0002413882,0.04851012,0.01058211,0.2974027,0.1208788,0.443369,0.001902194],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8499944,0.00268711,0.1429959,0.0002091462,0.00004347586,0.0001923995,0.000009206151,0.00003518122,0.003833283],"genre_scores_gemma":[0.9960927,0.0001586433,0.003212513,0.00001054162,0.00007881593,0.0000341732,0.0000228624,0.000007095784,0.0003826804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6416363,"threshold_uncertainty_score":0.5043186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02359633721303129,"score_gpt":0.2853442128436758,"score_spread":0.2617478756306446,"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."}}