Using deep learning to predict and prevent health issues in COPD patients

DeepCOPD: Development and Implementation of Deep Learning to Predict and Prevent COPD Health Care Encounters

NIH-funded research Dartmouth College · NIH-10991384

This study is creating a helpful tool that uses smart technology to predict when patients with COPD might need medical care, so doctors can better support them and improve their health.

Quick facts

Grant typeR01 grant
Study typeNIH-funded research
Funding institutionDartmouth College NIH-funded
Lab location1 site (Hanover, United States)
Project IDNIH-10991384 on NIH RePORTER

What this research studies

This research aims to develop a decision-support tool that utilizes advanced machine learning and deep learning algorithms to predict healthcare encounters for patients with Chronic Obstructive Pulmonary Disease (COPD). By analyzing electronic health records, the tool will identify at-risk patients and forecast potential medical events, such as hospital admissions and disease progression. This proactive approach allows caregivers to allocate resources more effectively and improve patient outcomes. The ultimate goal is to reduce the burden of COPD on both patients and the healthcare system.

Who could benefit from this research

Good fit: Ideal candidates for this research are individuals diagnosed with Chronic Obstructive Pulmonary Disease (COPD), particularly those experiencing frequent health issues.

Not a fit: Patients without a diagnosis of COPD or those with very mild symptoms may not benefit from this research.

Why it matters

Potential benefit: If successful, this research could significantly reduce hospitalizations and improve the quality of life for COPD patients by enabling timely interventions.

How similar studies have performed: Previous research has shown promise in using machine learning techniques for predicting health outcomes in chronic diseases, indicating a potential for success in this novel approach.

Where this research is happening

Hanover, United States

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.
Last reviewed 2026-06-09 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.