Point-of-care AI · Coming soon
All researchDeveloping a machine learning framework to automatically identify delirium from clinical chart text using large language models, and building a predictive model to detect delirium onset early using clinical and physiological biomarkers.
In progress
Coming soon. More information will appear here as the project progresses.
Related
This project develops generative deep learning models to reconstruct multi-lead ECG signals from minimal input leads, assessing whether a small number of key ECG recordings can capture sufficient signal and diagnostic information for robust clinical interpretation and scalable cardiac monitoring.
Point-of-care AI
Machine learning models identify high-risk acute coronary syndrome patients using admission ECG features and vital signs to predict in-hospital mortality and clinical deterioration, with interpretable outputs that guide triage and escalation decisions in acute cardiovascular care.
Point-of-care AI
The Northwell In-hospital Deterioration Model (NIDM) is an EHR-based deep learning model that continuously estimates a patient's risk of a deterioration event, unplanned ICU transfer, intubation, or death, within the next 48 hours from routinely collected electronic health record data. Deployed in silent mode inside Northwell's Epic environment for prospective monitoring, NIDM is built to surface the patient-specific factors behind each prediction, so care teams see not only who is at rising risk but why, early enough to act.
Point-of-care AI