{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T09:20:47Z","timestamp":1783761647404,"version":"3.55.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T00:00:00Z","timestamp":1674432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Encephalopathy is a severe co-morbid condition in critically ill patients that includes different clinical constellation of neurological symptoms. However, even for the most recognised form, delirium, this medical condition is rarely recorded in structured fields of electronic health records precluding large and unbiased retrospective studies. We aimed to identify patients with encephalopathy using a machine learning-based approach over clinical notes in electronic health records.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used a list of ICD-9 codes and clinical concepts related to encephalopathy to define a cohort of patients from the MIMIC-III dataset. Clinical notes were annotated with MedCAT and vectorized with a bag-of-word approach or word embedding using clinical concepts normalised to standard nomenclatures as features. Machine learning algorithms (support vector machines and random forest) trained with clinical notes from patients who had a diagnosis of encephalopathy (defined by ICD-9 codes) were used to classify patients with clinical concepts related to encephalopathy in their clinical notes but without any ICD-9 relevant code. A random selection of 50 patients were reviewed by a clinical expert for model validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among 46,520 different patients, 7.5% had encephalopathy related ICD-9 codes in all their admissions (group 1, definite encephalopathy), 45% clinical concepts related to encephalopathy only in their clinical notes (group 2, possible encephalopathy) and 38% did not have encephalopathy related concepts neither in structured nor in clinical notes (group 3, non-encephalopathy). Length of stay, mortality rate or number of co-morbid conditions were higher in groups 1 and 2 compared to group 3. The best model to classify patients from group 2 as patients with encephalopathy (SVM using embeddings) had F1 of 85% and predicted 31% patients from group 2 as having encephalopathy with a probability &amp;gt;90%. Validation on new cases found a precision ranging from 92% to 98% depending on the criteria considered.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Natural language processing techniques can leverage relevant clinical information that might help to identify patients with under-recognised clinical disorders such as encephalopathy. In the MIMIC dataset, this approach identifies with high probability thousands of patients that did not have a formal diagnosis in the structured information of the EHR.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2023.1085602","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T05:42:31Z","timestamp":1674452551000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Identifying encephalopathy in patients admitted to an intensive care unit: Going beyond structured information using natural language processing"],"prefix":"10.3389","volume":"5","author":[{"given":"Helena","family":"Ari\u00f1o","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soo Kyung","family":"Bae","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaya","family":"Chaturvedi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angus","family":"Roberts","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1007\/s00134-019-05907-4","article-title":"Updated nomenclature of delirium and acute encephalopathy: statement of ten societies","volume":"46","author":"Slooter","year":"2020","journal-title":"Intensive Care Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/s41572-020-00223-4","article-title":"Delirium","volume":"6","author":"Wilson","year":"2020","journal-title":"Nat Rev Dis Primers"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e420","DOI":"10.1136\/bmj.e420","article-title":"Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study","volume":"344","author":"van den Boogaard","year":"2012","journal-title":"BMJ"},{"key":"B4","doi-asserted-by":"publisher","first-page":"e181018","DOI":"10.1001\/jamanetworkopen.2018.1018","article-title":"Development and validation of an electronic health record-based machine learning model to estimate delirium risk in newly hospitalized patients without known cognitive impairment","volume":"1","author":"Wong","year":"2018","journal-title":"JAMA Netw Open"},{"key":"B5","year":""},{"key":"B6","doi-asserted-by":"publisher","first-page":"2703","DOI":"10.1001\/jama.286.21.2703","article-title":"Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU)","volume":"286","author":"Ely","year":"2001","journal-title":"JAMA"},{"key":"B7","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1186\/s13613-020-00723-2","article-title":"Cognitive and psychosocial outcomes of mechanically ventilated intensive care patients with and without delirium","volume":"10","author":"Bulic","year":"2020","journal-title":"Ann Intensive Care"},{"key":"B8","year":""},{"key":"B9","first-page":"912","article-title":"Accuracy and completeness of clinical coding using ICD-10 for ambulatory visits","volume":"2017","author":"Horsky","year":"2017","journal-title":"AMIA Annu Symp Proc"},{"key":"B10","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1186\/s12911-021-01461-6","article-title":"A novel model to label delirium in an intensive care unit from clinician actions","volume":"21","author":"Coombes","year":"2021","journal-title":"BMC Med Inform Decis Mak"},{"key":"B11","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1002\/pds.4226","article-title":"Evaluation of algorithms to identify delirium in administrative claims and drug utilization database","volume":"26","author":"Kim","year":"2017","journal-title":"Pharmacoepidemiol Drug Saf"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1097\/00003246-200107000-00012","article-title":"Evaluation of delirium in critically ill patients: validation of the confusion assessment method for the intensive care unit (CAM-ICU)","volume":"29","author":"Ely","year":"2001","journal-title":"Crit Care Med"},{"key":"B13","doi-asserted-by":"publisher","first-page":"ooac042","DOI":"10.1093\/jamiaopen\/ooac042","article-title":"A machine learning approach to identifying delirium from electronic health records","volume":"5","author":"Kim","year":"2022","journal-title":"JAMIA Open"},{"key":"B14","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s12871-021-01543-y","article-title":"Postoperative delirium prediction using machine learning models and preoperative electronic health record data","volume":"22","author":"Bishara","year":"2022","journal-title":"BMC Anesthesiol"},{"key":"B15","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s10916-018-1109-0","article-title":"Prediction of incident delirium using a random forest classifier","volume":"42","author":"Corradi","year":"2018","journal-title":"J Med Syst"},{"key":"B16","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/s11606-020-06238-7","article-title":"Machine learning to develop and internally validate a predictive model for post-operative delirium in a prospective, observational clinical cohort study of older surgical patients","volume":"36","author":"Racine","year":"2021","journal-title":"J Gen Intern Med"},{"key":"B17","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/s00134-022-06650-z","article-title":"Natural language processing diagnosed behavioral disturbance vs confusion assessment method for the intensive care unit: prevalence, patient characteristics, overlap, and association with treatment and outcome","volume":"48","author":"Young","year":"2022","journal-title":"Intensive Care Med"},{"key":"B18","doi-asserted-by":"publisher","first-page":"34","DOI":"10.3928\/00989134-20150723-01","article-title":"The language of delirium: keywords for identifying delirium from medical records","volume":"41","author":"Puelle","year":"2015","journal-title":"J Gerontol Nurs"},{"key":"B19","doi-asserted-by":"publisher","first-page":"946387","DOI":"10.3389\/fpsyt.2022.946387","article-title":"Natural language processing in clinical neuroscience and psychiatry: a review","volume":"13","author":"Crema","year":"2022","journal-title":"Front Psychiatry"},{"key":"B20","doi-asserted-by":"publisher","first-page":"e37557","DOI":"10.2196\/37557","article-title":"Automatic international classification of diseases coding system: deep contextualized language model with rule-based approaches","volume":"10","author":"Chen","year":"2022","journal-title":"JMIR Med Inform"},{"key":"B21","doi-asserted-by":"publisher","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci Data"},{"key":"B22","doi-asserted-by":"publisher","first-page":"D267","DOI":"10.1093\/nar\/gkh061","article-title":"The unified medical language system (UMLS): integrating biomedical terminology","volume":"32","author":"Bodenreider","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1101\/2020.05.09.084897","article-title":"Clinical knowledge graph integrates proteomics data into clinical decision-making","author":"Santos","year":"2020","journal-title":"bioRxiv"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102083","article-title":"Multi-domain clinical natural language processing with MedCAT: the medical concept annotation toolkit","volume":"117","author":"Kraljevic","year":"2021","journal-title":"Artif Intell Med"},{"key":"B25","doi-asserted-by":"publisher","first-page":"e054414","DOI":"10.1136\/bmjopen-2021-054414","article-title":"Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the south London and maudsley NHS foundation trust biomedical research centre (SLAM BRC) case register","volume":"12","author":"Bendayan","year":"2022","journal-title":"BMJ Open"},{"key":"B26","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1142\/9789811215636_0027","article-title":"Clinical concept embeddings learned from massive sources of multimodal medical data","volume":"25","author":"Beam","year":"2020","journal-title":"Pac Symp Biocomput"},{"key":"B27","doi-asserted-by":"publisher","first-page":"e104519","DOI":"10.1371\/journal.pone.0104519","article-title":"Validity of heart failure diagnoses in administrative databases: a systematic review and meta-analysis","volume":"9","author":"McCormick","year":"2014","journal-title":"PLoS One"},{"key":"B28","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1093\/ije\/dyx253","article-title":"A comparison of methods to correct for misclassification bias from administrative database diagnostic codes","volume":"47","author":"Walraven","year":"2018","journal-title":"Int J Epidemiol"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2023.1085602\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T05:42:35Z","timestamp":1674452555000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2023.1085602\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,23]]},"references-count":28,"alternative-id":["10.3389\/fdgth.2023.1085602"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2023.1085602","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,23]]},"article-number":"1085602"}}