Prediction of Incident Delirium Using a Random Forest classifier.
Delirium is a serious medical complication associated with poor outcomes. Given the complexity of the syndrome, prevention and early detection are critical in mitigating its effects. We used Confusion Assessment Method (CAM) screening and Electronic Health Record (EHR) data for 64,038 inpatient visi...
| Published in: | Journal of Medical Systems Vol. 42; no. 12; pp. 1 - 2 |
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| Main Authors: | , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133352450&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133352450 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Dec2018 vid: 42 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133352450 133352450 133352450 10.1007/s10916-018-1109-0 133352450 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of Incident Delirium Using a Random Forest classifier. aug: au: Corradi, John P. Thompson, Stephen Mather, Jeffrey F. Waszynski, Christine M. Dicks, Robert S. affil: Research Department, Hartford Hospital, 80 Seymour Street, ERD-223W, 06102, Hartford, CT, USA sug: subj: Delirium Epidemiology Electronic Health Records Decision Support Techniques Machine Learning Human Male Female Middle Age Aged Aged, 80 and Over Retrospective Design Algorithms Questionnaires Probability Data Mining Comorbidity Quality Improvement United States Delirium Prevention and Control Health Screening Early Diagnosis Decision Support Systems, Clinical Incidence Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Delirium is a serious medical complication associated with poor outcomes. Given the complexity of the syndrome, prevention and early detection are critical in mitigating its effects. We used Confusion Assessment Method (CAM) screening and Electronic Health Record (EHR) data for 64,038 inpatient visits to train and test a model predicting delirium arising in hospital. Incident delirium was defined as the first instance of a positive CAM occurring at least 48 h into a hospital stay. A Random Forest machine learning algorithm was used with demographic data, comorbidities, medications, procedures, and physiological measures. The data set was randomly partitioned 80% / 20% for training and validating the predictive model, respectively. Of the 51,240 patients in the training set, 2774 (5.4%) experienced delirium during their hospital stay; and of the 12,798 patients in the validation set, 701 (5.5%) experienced delirium. Under-sampling of the delirium negative population was used to address the class imbalance. The Random Forest predictive model yielded an area under the receiver operating characteristic curve (ROC AUC) of 0.909 (95% CI 0.898 to 0.921). Important variables in the model included previously identified predisposing and precipitating risk factors. This machine learning approach displayed a high degree of accuracy and has the potential to provide a clinically useful predictive model for earlier intervention in those patients at greatest risk of developing delirium. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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