Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

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

Award Number: R01HL157130
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 12/20/2021
PERIOD OF PERFORMANCE END DATE: 11/30/2026

Group Awards By:

View Award Description

DeepCOPD: Development and Implementation of Deep Learning to Predict and Prevent COPD Health Care Encounters - In the US, ~24 million persons live with COPD, half undiagnosed, and ~150,000 die of COPD annually. COPD causes over 700,000 US hospitalizations and costs nearly $50 billion per year. The human and financial burdens of COPD could likely be reduced if disease progression and other adverse events could be anticipated, enabling caregivers to focus finite resources on at-risk patients. We propose to create a decision-support tool that integrates biomedical informatics with advanced machine learning (ML) and deep learning (DL) algorithms to predict acute and chronic healthcare encounters (hospital admissions, readmissions, and ED encounters) and major disease progression events (home oxygen therapy) for outpatients with COPD. Such a tool would confer immediate clinical benefits and accelerate research on COPD disease progression and treatment. Predictive modeling is widely used to identify high-risk patients for care management in COPD and other disorders, with a strong emphasis on readmission risk. However, extant techniques are not sufficiently accurate and do not identify the specific nature of likely future medical events, estimate time-to-event, and specifically forecast medical encounters and disease progression events for individuals with COPD. Recent research in disease progression modeling support the application of DL and other ML methods to electronic health records (EHRs) to predict aspects of health history. EHRs contain both readily accessible structured data (e.g., lab results in well-defined fields) and unstructured texts such as physician’s notes. Unstructured texts contain a great deal of clinical information, but this information is laborious to access; impeding its routine use in research and the clinic. This has motivated attempts to use natural language processing (NLP) methods to automate annotation. We will apply NLP to identify symptoms, treatments, procedures, diagnoses, social risk factors, and functional status from clinical notes, expanding the data available from EHRs far beyond the usual coded variables. Also, and distinctively, we will carry out a stepped-wedge clinical implementation of the proposed predictive tool and evaluate its performance, a first for ML and DL prediction of COPD health events. Therefore, we propose four Specific Aims: AIM 1: Transform EHR data streams to provision patient-level feature sets for ML and DL consumption. AIM 2: Develop a set of ML and DL models to predict the time-to-event for home oxygen therapy initiation and healthcare encounters among patients with COPD. AIM 3: To develop and implement a prospective performance surveillance and calibration maintenance system to maintain the final Aim 2 model for each outcome. AIM 4: Evaluate adoption and usability of the DeepCOPD toolkit in near-realtime clinical use in two healthcare systems. The application is responsive to the NHLBI IDEA2Health (NOT-HL-19-712).


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $705,157 )
2025	2025	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Lung Diseases Research	002	4	9/4/2025	NON-COMPETING CONTINUATION	$0
2025	2025	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Lung Diseases Research	000	4	12/6/2024	NON-COMPETING CONTINUATION	$634,641
2025	2025	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Lung Diseases Research	001	4	8/12/2025	NON-COMPETING CONTINUATION	$70,516
														Subtotal = $705,157

Issue Date FY: 2024 ( Subtotal = $693,535 )
2024	2024	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Lung Diseases Research	000	3	11/22/2023	NON-COMPETING CONTINUATION	$636,921
2024	2024	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Lung Diseases Research	001	3	6/25/2024	NON-COMPETING CONTINUATION	$56,614
														Subtotal = $693,535

Issue Date FY: 2023 ( Subtotal = $741,611 )
2023	2023	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Lung Diseases Research	001	2	3/6/2023	NON-COMPETING CONTINUATION	$74,161
2023	2023	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Lung Diseases Research	000	2	11/25/2022	NON-COMPETING CONTINUATION	$667,450
														Subtotal = $741,611

Issue Date FY: 2022 ( Subtotal = $786,943 )
2022	2022	TRUSTEES OF DARTMOUTH COLLEGE	6001 PARKHURST HALL STE 207	HANOVER	NH	03755	GRAFTON	USA	Lung Diseases Research	000	1	12/17/2021	NEW	$786,943
2022	2022	TRUSTEES OF DARTMOUTH COLLEGE	7 LEBANON ST	HANOVER	NH	03755	GRAFTON	USA	Lung Diseases Research	001	1	8/27/2022	NEW	$0
														Subtotal = $786,943

Grand Total All Awards = $2,927,246

Top

All Categories

About

Search

Reports

Data Submission

Award Information

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

Award Number: R01HL157130

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 12/20/2021

PERIOD OF PERFORMANCE END DATE: 11/30/2026

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer