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Division of Health AIDivision of Health AI
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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.

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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.

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Researcher · Division of Health AI

Diego Gonzalez Garcia-Torres

Diego Gonzalez Garcia-Torres is a Deployment Engineer at the Division of Health AI, part of the Feinstein Institutes for Medical Research at Northwell Health. He builds the software and infrastructure that move the division's models out of research and into the hospital, and does the work that keeps them running: data pipelines, deployment, monitoring, security review, and integration with clinical systems. He shadows physicians in the hospital to keep the work grounded in how care is actually delivered.

He supports the EHR-based model for predicting in-hospital deterioration, now running silently across multiple Northwell hospitals, and a nursing workforce model that forecasts staffing demand and attrition up to 12 months ahead. He also leads a non-contact neonatal monitoring project: a bedside system that pairs a thermal and an RGB camera to measure heart rate, breathing, and temperature and to detect apnea in premature infants without touching them, in development for the Cohen Children's NICU and funded through NIH and philanthropic grants he helped write. Alongside these he builds internal tools for the lab, from a chart-annotation app for physicians to an ECG screening tool and utilities for grant preparation and model training, working across the stack from Python and cloud training jobs to React and FastAPI.

2 projects·0 papers

Diego Gonzalez Garcia-Torres
Role
Deployment Engineer

Research projects

Research projects

In-hospital deterioration prediction (EHR)

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

DEMANDATTRITION

Nursing workforce optimization

Machine learning models using DeepAR probabilistic forecasting predict nursing workforce demand across Northwell's hospital units up to 12 months ahead, supporting preemptive hiring and staffing decisions across diverse specialties.

Operational AI

Related

More of the division

Full roster →

Todd Levy, MS

Senior Biomedical Engineer

Shubham Debnath, PhD

Senior Research Scientist

Michael Scheid, PhD

Assistant Investigator, Senior Biomedical Engineer