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Identifying and addressing missingness and bias to enhance discovery from multimodal health data

Award Number: R01LM014239
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 05/10/2023
PERIOD OF PERFORMANCE END DATE: 02/28/2027

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Identifying and addressing missingness and bias to enhance discovery from multimodal health data - PROJECT SUMMARY Recent successes of machine learning (especially deep learning) in analyzing electronic health record (EHR) data have not only stimulated excitement in stake holders but have also raised concerns potential unfair or biased clinical decision making facilitated by machine learning. A number of fairness measurements have been proposed. However, they underappreciate the chronical systematic differences between the distributions of protected and unprotected groups. Hence, when used to develop machine learning methods, they may worsen within-group issues and dampen performance of the trained machine learning models. The situation can be further complicated by missing values that are common in EHR data, which will exacerbate unfairness if not handled properly. In this project, we aim to develop a novel fairness evaluation methodology (Aim 1) and incorporate it into the development of innovative machine learning models and techniques to reduce biases and increase interpretability (Aim 2). To better and more fairly handle missing values, we will develop new machine learning models that contain trainable in-process missing value imputation components and new algorithms to train them with constraints defined by our new fairness evaluation method (Aim 3). In addition, we will develop proactive machine learning techniques to advance heath equity (Aim 4). We will evaluate and improve our new fairness measurements and machine learning techniques in the context of facilitating clinical decision making (Aim 5). Large datasets from two of the largest US healthcare systems will be used in carrying out the proposed research.


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 = $387,919 )
2025	2025	BRIGHAM & WOMENS HOSPITAL INC	75 FRANCIS ST	BOSTON	MA	02115	SUFFOLK	USA	Medical Library Assistance	000	3	2/19/2025	NON-COMPETING CONTINUATION	$349,127
2025	2025	BRIGHAM & WOMENS HOSPITAL INC	75 FRANCIS ST	BOSTON	MA	02115	SUFFOLK	USA	Medical Library Assistance	001	3	6/26/2025	NON-COMPETING CONTINUATION	$38,792
														Subtotal = $387,919

Issue Date FY: 2024 ( Subtotal = $387,919 )
2024	2024	BRIGHAM & WOMENS HOSPITAL INC	75 FRANCIS ST	BOSTON	MA	02115	SUFFOLK	USA	Medical Library Assistance	001	2	5/8/2024	NON-COMPETING CONTINUATION	$38,792
2024	2024	BRIGHAM & WOMENS HOSPITAL INC	75 FRANCIS ST	BOSTON	MA	02115	SUFFOLK	USA	Medical Library Assistance	000	2	2/29/2024	NON-COMPETING CONTINUATION	$349,127
														Subtotal = $387,919

Issue Date FY: 2023 ( Subtotal = $400,559 )
2023	2023	BRIGHAM & WOMENS HOSPITAL INC	75 FRANCIS ST	BOSTON	MA	02115	SUFFOLK	USA	Medical Library Assistance	000	1	5/9/2023	NEW	$400,559
														Subtotal = $400,559

Grand Total All Awards = $1,176,397

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Identifying and addressing missingness and bias to enhance discovery from multimodal health data

Award Number: R01LM014239

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 05/10/2023

PERIOD OF PERFORMANCE END DATE: 02/28/2027

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