{"id":"W4234577219","doi":"10.2196/33834","title":"Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Intensive Care Unit Cognitive Disorders","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute on Aging; American Academy of Sleep Medicine; James S. McDonnell Foundation; National Institutes of Health; Glenn Foundation for Medical Research; Fondation pour la Recherche Médicale","keywords":"Delirium; Receiver operating characteristic; Medicine; Artificial intelligence; Machine learning; Confusion; Confusion matrix; Medical record; Observational study; Gold standard (test); Clinical decision support system; Health records; Natural language processing; Internal medicine; Intensive care medicine; Computer science; Decision support system; Psychology; Health care","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":[0.002023925,0.000502211,0.0009087598,0.00097683,0.0008109207,0.001118128,0.0007027344,0.0008328342,0.0013538],"category_scores_gemma":[0.009906993,0.0006617864,0.001036987,0.001089811,0.0005950766,0.001511978,0.001516672,0.001285662,0.0003962886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007153504,"about_ca_system_score_gemma":0.0009857077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005718205,"about_ca_topic_score_gemma":0.007104806,"domain_scores_codex":[0.9975377,0.0006313656,0.0004988216,0.0006801523,0.0004452421,0.0002066274],"domain_scores_gemma":[0.9914351,0.002253751,0.003313571,0.0007639535,0.001337983,0.0008956359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003399542,0.0003411481,0.9972557,0.00006367746,0.0001379498,0.0001520063,0.0003419931,0.00004472099,0.0001142929,0.00001597456,0.0002044334,0.0009880982],"study_design_scores_gemma":[0.000135341,0.001055316,0.9943718,0.00007827356,0.0001857405,0.0006113026,0.00210108,0.0009182048,0.00009402695,0.0000682172,0.000349298,0.00003134424],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992274,0.0001227031,0.0001219838,0.0000216523,0.000005402832,0.00004521286,0.0003417839,0.000002362095,0.0001116578],"genre_scores_gemma":[0.9984667,0.0001270721,0.0002430675,0.00008502215,0.00001506611,0.00005957199,0.0009433662,0.000003222877,0.00005687018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005718205,"threshold_uncertainty_score":0.01136982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2161903191724032,"score_gpt":0.4861274371908355,"score_spread":0.2699371180184323,"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."}}