{"id":"W4381094908","doi":"10.2196/48297","title":"Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences","keywords":"Interoperability; Computer science; Health care; Data science; Knowledge management; Software engineering; World Wide Web","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":[0.03296864,0.001132047,0.002691098,0.02159848,0.001069448,0.005490004,0.003387896,0.003582557,0.003720924],"category_scores_gemma":[0.1121354,0.001011139,0.004245183,0.01998737,0.002134287,0.007181362,0.003788348,0.002687711,0.001063244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00558966,"about_ca_system_score_gemma":0.02967218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008756101,"about_ca_topic_score_gemma":0.009022837,"domain_scores_codex":[0.9800088,0.006281648,0.007970321,0.001189315,0.004123414,0.0004265349],"domain_scores_gemma":[0.8357316,0.1293171,0.01329084,0.00407756,0.01685887,0.0007238887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00008201595,0.00006314258,0.001280398,0.4503221,0.00117729,0.0001314434,0.0007978664,0.001453477,0.0003989946,0.01320514,0.01303045,0.5180576],"study_design_scores_gemma":[0.0000296149,0.0000824323,0.001716173,0.7907181,0.002542012,0.0003010177,0.0004906101,0.0007058909,0.0006256197,0.005842172,0.1968824,0.0000640244],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004746293,0.9912689,0.002540442,0.002607322,0.0003722971,0.0003287849,0.0003667965,0.00005095932,0.001989887],"genre_scores_gemma":[0.007236123,0.981311,0.007991506,0.001476329,0.0002481103,0.0008065367,0.0007244035,0.00002330462,0.0001825824],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.03296864,"threshold_uncertainty_score":0.1743569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2094021149431455,"score_gpt":0.5546051165683983,"score_spread":0.3452030016252527,"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."}}