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

Modeling the Incompleteness and Biases of Health Data

Award Number: R01LM013337
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 06/01/2020
PERIOD OF PERFORMANCE END DATE: 03/31/2025

Group Awards By:

View Award Description

Modeling the Incompleteness and Biases of Health Data - Modeling the Incompleteness and Biases of Health Data Researchers are increasingly working to “mine” health data to derive new medical knowledge. Unlike experimental data that are collected per a research protocol, the primary role of clinical data is to help clinicians care for patients, so the procedures for its collection are not often systematic. Thus, missing and/or biased data can hinder medical knowledge discovery and data mining efforts. Existing efforts for missing health data imputation often focus on only cross-sectional correlation (e.g., correlation across subjects or across variables) but neglect autocorrelation (e.g., correlation across time points). Moreover, they often focus on modeling incompleteness but neglect the biases in health data. Modeling both the incompleteness and bias may contribute to better understanding of health data and better support clinical decision making. We propose a novel framework of Bias-Aware Missing data Imputation with Cross-sectional correlation and Autocorrelation (BAMICA), and leverage clinical notes to better inform the methods that will otherwise rely on structured health data only. In addition to evaluating its imputation accuracy, we will apply the proposed framework to assist in downstream tasks such as predictive modeling for multiple outcomes across a diverse range of clinical and cohort study datasets. Aim 1 introduces the MICA framework to jointly consider cross-sectional correlation and auto-correlation. In Aim 2, we will augment MICA to be bias-aware (hence BAMICA) to account for biases stemmed from multiple roots such as healthcare process and use them as features in imputing missing health data. This augmentation is achieved by a novel recurrent neural network architecture that keeps track of both evolution of health data variables and bias factors. In Aim 3, we will supplement unstructured clinical notes to structured health data for modeling incompleteness and biases using a novel architecture of graph neural network on top of memory network. We will apply graph neural networks to process clinical notes in order to learn proper representations as input to the memory networks for imputation and downstream predictive modeling tasks. Depending on the clinical problem and data availability, not all modules may be needed. Thus our proposed BAMICA framework is designed to be flexible and consists of selectable modules to meet some or all of the above needs. In summary, our proposal bridges a key knowledge gap in jointly modeling incompleteness and biases in health data and utilizes unstructured clinical notes to supplement and augment such modeling in order to better support predictive modeling and clinical decision making. We will demonstrate generalizability by experimenting on four large clinical and cohort study datasets, and by scaling up to the eMERGE network spanning 11 institutions nationwide. We will disseminate the open-source framework. The principled and flexible framework generated by this project will bring significant methodological advancement and have a direct impact on enhancing discovery from health data.


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: 2026 ( Subtotal = -$30 )
2026	2023	NORTHWESTERN UNIVERSITY	633 CLARK ST	EVANSTON	IL	60208	COOK	USA	Medical Library Assistance	000	4	2/5/2026	NON-COMPETING CONTINUATION	-$30
														Subtotal = -$30

Issue Date FY: 2023 ( Subtotal = $307,470 )
2023	2023	NORTHWESTERN UNIVERSITY	633 CLARK ST	EVANSTON	IL	60208	COOK	USA	Medical Library Assistance	000	4	3/2/2023	NON-COMPETING CONTINUATION	$307,470
														Subtotal = $307,470

Issue Date FY: 2022 ( Subtotal = $311,339 )
2022	2022	NORTHWESTERN UNIVERSITY	633 CLARK ST	EVANSTON	IL	60208	COOK	USA	Medical Library Assistance	000	3	3/1/2022	NON-COMPETING CONTINUATION	$311,339
														Subtotal = $311,339

Issue Date FY: 2021 ( Subtotal = $315,727 )
2021	2021	NORTHWESTERN UNIVERSITY	633 CLARK ST	EVANSTON	IL	60208	COOK	USA	Medical Library Assistance	001	2	4/28/2021	NON-COMPETING CONTINUATION	$15,788
2021	2021	NORTHWESTERN UNIVERSITY	633 CLARK ST	EVANSTON	IL	60208	COOK	USA	Medical Library Assistance	000	2	4/1/2021	NON-COMPETING CONTINUATION	$299,939
														Subtotal = $315,727

Issue Date FY: 2020 ( Subtotal = $348,397 )
2020	2020	NORTHWESTERN UNIVERSITY	633 CLARK ST	EVANSTON	IL	60208	COOK	USA	Medical Library Assistance	000	1	5/20/2020	NEW	$348,397
														Subtotal = $348,397

Grand Total All Awards = $1,282,903

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Modeling the Incompleteness and Biases of Health Data

Award Number: R01LM013337

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 06/01/2020

PERIOD OF PERFORMANCE END DATE: 03/31/2025

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer