{"id":"W4366463885","doi":"10.2196/44977","title":"Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; U.S. National Library of Medicine; National Institute on Drug Abuse; Institute for Clinical and Translational Research, University of Wisconsin, Madison; National Institutes of Health","keywords":"Computer science; Workflow; Clinical decision support system; Pipeline (software); Cloud computing; Analytics; Software deployment; Triage; Artificial intelligence; Health care; Machine learning; Decision support system; Medicine; Data science; Database; Software engineering; Medical emergency","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":[0.005262451,0.0001298211,0.0003153358,0.0002171063,0.00009184343,0.00005554898,0.0005292998,0.0001079921,0.00001225859],"category_scores_gemma":[0.0005912947,0.00009422922,0.00004237389,0.0005996972,0.00004430037,0.0004237758,0.0001591931,0.0006102968,0.000004180833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008248754,"about_ca_system_score_gemma":0.0004246591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004882273,"about_ca_topic_score_gemma":0.00137138,"domain_scores_codex":[0.9968745,0.0003353153,0.001420067,0.0001922997,0.0006883331,0.0004894538],"domain_scores_gemma":[0.9984371,0.0006355695,0.0004472581,0.0002489183,0.00007509346,0.0001560364],"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":[0.0000739887,0.00005037033,0.01896121,0.0003450072,0.000002217281,0.000003499063,0.02996873,0.00002659305,9.258171e-7,0.0001047599,0.0003269818,0.9501357],"study_design_scores_gemma":[0.002393695,0.00106398,0.0457901,0.0002556496,0.000001977538,0.000009590008,0.005977598,0.9436941,0.000002207806,0.0001278109,0.0005752654,0.0001080798],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808245,0.0001088901,0.01642622,0.001396029,0.00007674586,0.001037366,0.000002273545,0.0001090885,0.00001890982],"genre_scores_gemma":[0.971864,0.000567855,0.02615345,0.001007706,0.00006808156,0.000121353,0.0001916755,0.00001440811,0.0000114262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9500276,"threshold_uncertainty_score":0.3842556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01867685295521919,"score_gpt":0.418264490297682,"score_spread":0.3995876373424628,"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."}}