Machine learning to detect and predict delirium in hospitalized older adults
Official title: Machine Learning Models to Identify and Predict Delirium in Hospitalized Older Adults
Funded at Feinstein Institute for Medical Research · NIH-11341647 · Data updated 2026-11-12
A doctors and researchers team will use computer models to spot delirium in older adults in the hospital and predict who might develop it, helping prevent problems.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Feinstein Institute for Medical Research NIH-funded |
| Lab location | 1 site (Manhasset, United States) |
| Project start | September 1, 2026 |
| Project end | May 31, 2031 |
| Funding year | 2026 |
| Award (this year) | $698,456 |
| NIH project number | 1R01AG100193-01 |
| Project ID | NIH-11341647 on NIH RePORTER |
Who could benefit from this research
✓Could help
Older adults admitted to hospital who are at risk of or developing delirium, particularly those seen in the emergency department.
✕Less likely to help
Patients who do not develop delirium or do not fit the hospital setting may receive no direct benefit.
What this research studies
This project aims to use machine learning to identify delirium in older adults when they come through the emergency department and during their hospital stay. The approach will analyze routine patient data to flag those at risk so clinicians can start prevention measures early. The study builds on prior findings that delirium is often missed in the ED and early hospitalization. By predicting who is at risk, hospitals can allocate resources more efficiently and reduce complications.
Why it matters
Potential benefit: If successful, it could lead to earlier delirium prevention and better outcomes for hospitalized older adults.
How similar studies have gone: Previous work suggests ML can aid in delirium detection and risk prediction, with potential for improvement in accuracy and timeliness.
Where this research is happening
-
Feinstein Institute for Medical Research
Manhasset, United States Active
Researchers
- Principal investigator: Sinvani, Liron Danay — Feinstein Institute for Medical Research
- Study coordinator: Sinvani, Liron Danay
Related research
Other NIH-funded projects on the same topics.
-
READI-SET-GO: Improving Delirium Screening for Hospitalized Older Adults
A hospital-based program tests how well routine nurse delirium screening using UB-CAM works when integrated into the electronic health record to improve outcomes for older patients.
-
Continuous wearable monitor with AI to prevent agitation and delirium in hospitalized older adults
A wearable sensor and AI system will continuously track agitation in older hospital patients to help prevent delirium and protect brain health.
-
Low brain changes from anesthesia may signal early Alzheimer's risk
A study at Stanford will look at whether brain responses to anesthesia during surgery reveal early brain changes linked to Alzheimer’s disease and delirium risk in people aged 65 and up.
-
Why dementia and sudden confusion (delirium) often happen together
This project looks at brain electrical signals and chemical changes to find why people with dementia sometimes develop sudden confusion called delirium.
-
AKI-related delirium after heart surgery
This grant funds research to understand why some people develop delirium after heart surgery when they also have acute kidney injury.
-
Lipocalin-2 and delirium after surgery in Alzheimer’s disease and related dementias
A study at Duke University looks at how an inflammatory protein called lipocalin-2 may trigger delirium after surgery, especially in people at risk for Alzheimer’s and related dementias.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.