What is the Delirium Bibliography? The searchable Delirium Bibliography page is one of our most popular features, allowing you to quickly gain access to the literature on delirium and acute care of older persons. It is primarily intended for clinicians and researchers interested in exploring these topics. The NIDUS team keeps it updated for you on a monthly basis!
How to Search for Articles: Search by author, title, year, and/or keywords. Each article is indexed by keywords taken from MEDLINE and other relevant databases. Click on the title of the article to read the abstract, journal, etc.
Reference Information
- Title
- Developing a machine learning model to identify delirium risk in geriatric internal medicine inpatients
- Authors
- Li, Q. Zhao, Y. Chen, Y. Yue, J. Xiong, Y.
- Year
- 2021
- Journal
- Eur Geriatr Med
- Abstract
PURPOSE: To develop a machine learning model that predicts delirium risk in geriatric internal medicine inpatients. METHODS: A prospective cohort study of internal medicine wards in a tertiary care hospital in China. Blinded observers assessed delirium using the Confusion Assessment Method (CAM). The data set was randomly divided into a training set (70%) and a test set (30%). The model was trained on the training set using the decision tree and the five-fold cross-validation, and then the model performance was evaluated on the test set. Under-sampling was used to address the class imbalance. The discriminatory power of the model was measured by the area under the receiver operating characteristic curve (AUC) and F1 score. The data set comprised 740 patients from March 2016 to January 2017. RESULTS: The training set included 518 patients; the median (IQR) age was 84 (79-87) years; 364 (70.3%) were men; 71 (13.7%) with delirium. The test set included 222 patients; the median (IQR) age was 84.5 (79-87) years; 163 (73.4%) were men; 30 (13.5%) with delirium. In total, the data set included 740 hospital admissions with a median (IQR) age of 84 (79-87) years, 527 (71.2%) were men, and 101 (13.6%) with delirium. From 32 potential predictors, we included five variables in the predictive model: depression, cognitive impairment, types of drugs, nutritional status, and activity of daily life (ADL). The mean AUC on the training set was 0.967, the AUC and F1 score on the test set was 0.950 and 0.810, respectively. The model achieved 93.3% sensitivity, 94.3% specificity, 71.8% positive predictive value, 98.9% negative predictive value, and 94.1% accuracy on the test set. CONCLUSION: This machine learning model may allow more precise targeting of delirium prevention and could support clinical decision making in geriatric internal medicine wards.
- PMID
- Keywords
Delirium
Elderly
Internal medicine
Machine learning
Predictive model
- Page(s)
- Volume
- Issue
| Title | Authors | Journal | Year | Keywords |
|---|---|---|---|---|
| Undiagnosed delirium is frequent and difficult to predict: Results from a prevalence survey of a tertiary hospital. | Lange, P. W. Lamanna, M. Watson, R. Maier, A. B. | J Clin Nurs | 2019 |
Undiagnosed delirium |