ArticleJournal of medical systems2018
Prediction of Incident Delirium Using a Random Forest classifier.
Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction Models for In-Hospital Delirium Using Routinely Collected Electronic Health Record Data: Systematic Review.JMIR medical informatics · 2026Pooled it
- Development and validation of a machine learning-based model for predicting delirium risk in postoperative brain tumor patients in the Intensive Care Unit.Journal of neuro-oncology · 2026Article
- Research progress in agitation early warning monitoring and new technologies for critically ill patients: a narrative review.Frontiers in medicine · 2026Review
- Harnessing the Power of Technology to Transform Delirium Severity Measurement in the Intensive Care Unit: Protocol for a Prospective Cohort Study.JMIR research protocols · 2025Article
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- Development of a Disease Model for Predicting Postoperative Delirium Using Combined Blood Biomarkers.Annals of clinical and translational neurology · 2025Article
- Machine Learning Multimodal Model for Delirium Risk Stratification.JAMA network open · 2025Article
- Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation.JMIR medical informatics · 2025Article
- Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.Journal of medical Internet research · 2025Article
- Enhancing Delirium Prediction and Prevention in Elderly Patients Through Machine Learning-Based Analysis.Sultan Qaboos University medical journal · 2025Article
- Applying a transformer architecture to intraoperative temporal dynamics improves the prediction of postoperative delirium.Communications medicine · 2024Article
- Machine learning-based delirium prediction in surgical in-patients: a prospective validation study.JAMIA open · 2024Article
- Understanding overfitting in random forest for probability estimation: a visualization and simulation study.Diagnostic and prognostic research · 2024Article
- Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups.International journal of clinical pharmacy · 2024Article
- DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records.BMC cardiovascular disorders · 2024Article
- Using Machine Learning and Electronic Health Records to Identify Neuropsychiatric Risk Scores for Delirium in ICU and General Hospital Settings.Neuropsychiatric disease and treatment · 2024Article
- Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models.BMJ health & care informatics · 2023Article
- Leveraging artificial intelligence for the management of postoperative delirium following cardiac surgery.European journal of anaesthesiology and intensive care · 2023Article
- Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future.Clinical interventions in aging · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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