{"id":"W4409357119","doi":"10.2196/63157","title":"A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Institute on Aging","keywords":"Preprint; Computer science; Health records; Artificial intelligence; Algorithm; Electronic health record; Machine learning; Health care; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008686042,0.0001529257,0.0002683426,0.0002889133,0.0002735238,0.0001468645,0.0003117804,0.00007252888,0.000002830567],"category_scores_gemma":[0.0002314923,0.0001299164,0.00002314832,0.0006610515,0.00002648934,0.000380646,0.0001938233,0.0005994752,0.000001278224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002075201,"about_ca_system_score_gemma":0.001058081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001332646,"about_ca_topic_score_gemma":0.0001585528,"domain_scores_codex":[0.9981762,0.0001337403,0.0005928587,0.0002019976,0.0004412693,0.0004539337],"domain_scores_gemma":[0.9991178,0.0002257751,0.0002179526,0.0001906846,0.0000595242,0.0001882451],"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":[0.0000197649,0.0001407836,0.05799127,0.00246892,0.00002610269,0.000002237384,0.01320737,0.001423655,1.099589e-7,0.005357586,0.000152779,0.9192094],"study_design_scores_gemma":[0.0008052248,0.0002213162,0.008568877,0.0001988973,0.000003316411,0.00002943482,0.0002950394,0.9852628,0.000003874447,0.0002634055,0.004210604,0.0001372127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02652946,0.0002951146,0.9710365,0.0009305049,0.00008293457,0.000793589,0.000001323668,0.0001840895,0.0001464989],"genre_scores_gemma":[0.3741884,0.0003338696,0.6213737,0.002673195,0.00006604481,0.0009303074,0.0002352059,0.00002981582,0.0001694844],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9838392,"threshold_uncertainty_score":0.5297835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01445189973638178,"score_gpt":0.3120125799120765,"score_spread":0.2975606801756948,"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."}}