ArticleJournal of the American Medical Informatics Association : JAMIA2022
New onset delirium prediction using machine learning and long short-term memory (LSTM) in electronic health record.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction Models for In-Hospital Delirium Using Routinely Collected Electronic Health Record Data: Systematic Review.JMIR medical informatics · 2026Pooled it
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Multi-output LSTM-based prediction of postoperative delirium: integrating baseline and perioperative data for enhanced risk stratification in older spine surgery patients.BioData mining · 2026Article
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Predicting intracranial pressure monitor placement in children with traumatic brain injury: a prospective cohort study to develop a clinical decision support tool.Journal of the American Medical Informatics Association : JAMIA · 2026Observational
- Sleep Quality, Circadian Rhythm Stability and Changes in Delirium State in Predicting Mortality Risk in Intensive Care Unit Patients: A Prospective Observational Study.Nursing in critical care · 2025Observational
- Predicting ICU Delirium in Critically Ill COVID-19 Patients Using Demographic, Clinical, and Laboratory Admission Data: A Machine Learning Approach.Life (Basel, Switzerland) · 2025Article
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- Clinical Decision Support for Patient Cases with Asymptomatic Carotid Artery Stenosis Using AI Models and Electronic Medical Records.Journal of cardiovascular development and disease · 2025Article
- Automated classification of clinical diagnoses in electronic health records using transformer.PloS one · 2025Article
- Applying a transformer architecture to intraoperative temporal dynamics improves the prediction of postoperative delirium.Communications medicine · 2024Article
- A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory.Sensors (Basel, Switzerland) · 2024Article
- Leveraging explainable artificial intelligence to optimize clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Nurse Education in Care of Delirium: Achieving a Change from Transcription and Translation to Interpretation for Reduced Strain.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2024Article
- Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach.Journal of neuroengineering and rehabilitation · 2024Article
- Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy.Frontiers in neurology · 2023Article
- The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review.Digital healthArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveTo develop and test an accurate deep learning model for predicting new onset delirium in hospitalized adult patients.
methodsUsing electronic health record (EHR) data extracted from a large academic medical center, we developed a model combining long short-term memory (LSTM) and machine learning to predict new onset delirium and compared its performance with machine-learning-only models (logistic regression, random forest, support vector machine, neural network, and LightGBM). The labels of models were confusion assessment method (CAM) assessments. We evaluated models on a hold-out dataset. We calculated Shapley additive explanations (SHAP) measures to gauge the feature impact on the model.
resultsA total of 331 489 CAM assessments with 896 features from 34 035 patients were included. The LightGBM model achieved the best performance (AUC 0.927 [0.924, 0.929] and F1 0.626 [0.618, 0.634]) among the machine learning models. When combined with the LSTM model, the final model's performance improved significantly (P = .001) with AUC 0.952 [0.950, 0.955] and F1 0.759 [0.755, 0.765]. The precision value of the combined model improved from 0.497 to 0.751 with a fixed recall of 0.8. Using the mean absolute SHAP values, we identified the top 20 features, including age, heart rate, Richmond Agitation-Sedation Scale score, Morse fall risk score, pulse, respiratory rate, and level of care.
conclusionLeveraging LSTM to capture temporal trends and combining it with the LightGBM model can significantly improve the prediction of new onset delirium, providing an algorithmic basis for the subsequent development of clinical decision support tools for proactive delirium interventions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.