Skip to content
Division of Health AIDivision of Health AI
AboutTeamResearchPublicationsInternship
AboutTeamResearchPublicationsInternship
Division of Health AIDivision of Health AI

Clinical AI built with the data and clinicians of one of the largest health systems in the United States.

Explore

  • About
  • Team
  • Research
  • Publications
  • Internship

Affiliations

  • Feinstein Institutes↗ (opens in new tab)
  • Northwell Health↗ (opens in new tab)
  • Zucker School of MedicineHofstra Northwell

Located at

  • Institute of Health System Science
  • Institute of Bioelectronic Medicine
  • Manhasset, New York

© 2026 Division of Health AI, Northwell Health. All rights reserved.

Point-of-care AI · Closed

← All research

Overnight patient-stability monitoring

Past project

A deep learning model that predicts overnight patient stability from vital sign sequences, enabling the safe avoidance of unnecessary monitoring for 50% of patient-nights while misclassifying only 2 per 10,000 cases. Trained on 2.3 million admissions, it reached a prospective AUC of 0.971.

1 researcher·1 publication·3 figures

Point-of-care AIDivision of Health AI
ROC curves on retrospective and prospective data at three clinical thresholds, trading off letting stable patients sleep against catching instability.
ROC curves on retrospective and prospective data at three clinical thresholds, trading off letting stable patients sleep against catching instability. npj Digital Medicine, 2020 · CC BY 4.0 (opens in new tab)
Example vital-sign input sequence and the model's overnight stability prediction with its risk score.
Example vital-sign input sequence and the model's overnight stability prediction with its risk score. npj Digital Medicine, 2020 · CC BY 4.0 (opens in new tab)
Vital-sign distributions for correctly classified stable patients versus those misclassified as stable.
Vital-sign distributions for correctly classified stable patients versus those misclassified as stable. npj Digital Medicine, 2020 · CC BY 4.0 (opens in new tab)

From the papers

Published figures

Figures reproduced from the open-access papers behind this project.

Each panel keeps its original source and license. Scroll the column at left to move through the published results while these notes stay in view.

People · 1

On this project

Theodoros Zanos, PhD

Professor & AVP

Publications · 1

Related papers

View all →
npj Digital MedicineNov 2020

Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

(opens in new tab)

Related

More in Point-of-care AI

Generative AI for Minimal-Lead ECG Reconstruction

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

ACS RISKHIGHMODERATELOW

AI-Enabled ECG Risk Stratification for Acute Coronary Syndrome

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

ONSET

Delirium classification

Developing 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.

Point-of-care AI

←Previous

Letters and replies

Next→

Generative AI for Minimal-Lead ECG Reconstruction