{"id":"W3123984549","doi":"10.2196/17934","title":"Hybrid Deep Learning for Medication-Related Information Extraction From Clinical Texts in French: MedExt Algorithm Development Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Conditional random field; Natural language processing; Information extraction; Word embedding; Recall; Security token; Machine learning; Deep learning; F1 score; Artificial neural network; Task (project management); Recurrent neural network; Embedding","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":[0.00237777,0.001477467,0.0007670635,0.001567768,0.0003963197,0.001044709,0.001216834,0.00153267,0.003126767],"category_scores_gemma":[0.004568407,0.0003469192,0.0008738767,0.00113749,0.000296914,0.00130339,0.0007292489,0.001136338,0.0007661019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001742509,"about_ca_system_score_gemma":0.001909023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02155907,"about_ca_topic_score_gemma":0.0210893,"domain_scores_codex":[0.9990454,0.0003855148,0.00008511761,0.0002635183,0.0001199515,0.0001004608],"domain_scores_gemma":[0.9974715,0.00171042,0.0000857895,0.0001692528,0.0005021868,0.00006102167],"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":[0.000674672,0.0009659908,0.009327275,0.000461576,0.0004077725,0.0003814744,0.0002400045,0.2120387,0.00629952,0.00174499,0.008135106,0.7593229],"study_design_scores_gemma":[0.00009759207,0.0002053263,0.001918623,0.00003854105,0.00007873958,0.0001000213,0.00008027513,0.9884903,0.005676489,0.0007391354,0.00256098,0.000013862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6320521,0.005931585,0.3353255,0.001189562,0.0002030792,0.0007277169,0.002603909,0.01482186,0.007144765],"genre_scores_gemma":[0.6338508,0.001318224,0.3494862,0.0006551838,0.00007144955,0.0004779788,0.007044508,0.0003271419,0.006768489],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02155907,"threshold_uncertainty_score":0.04286712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02252608427090013,"score_gpt":0.3437960057843904,"score_spread":0.3212699215134903,"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."}}