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Statistical Methods for Incorporating Machine Learning Tools in Inference and Large-Scale Surveillance using Electronic Medical Records Data

Award Number: R01HL137808
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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SUMMARY The modernization and standardization of clinical care information systems is creating large networks of linked electronic health records (EHR) that capture key treatments and select patient outcomes for millions of patients throughout the country. The observational data emerging from these systems provide an unparalleled opportunity to learn about the effectiveness of existing and novel treatments, and to monitor potential safety issues that may arise when interventions are used in broad patient populations. However, observational clinical data have exposures that are driven by many factors and therefore aggressive adjustment is needed to remove as much confounding bias as possible in order to make attribution regarding select exposures. The field of machine learning provides a powerful collection of data-driven approaches for performing flexible, thorough confounding adjustment, but performing reliable statistical inference is particularly challenging when these techniques are used as part of the analytic strategy. We propose to advance reproducible research methods by developing and illustrating novel targeted learning tools that leverage the flexibility of machine learning methods to detect and characterize health effect signals using large-scale EHR data. Specifically, we will first develop techniques for making efficient, statistically valid and robust inference for treatment effects using state-of-the-art machine learning tools. We will also develop online learning techniques to make such inference in the context of streaming EHR data. Methodological advances will enable us to formulate a formal, rigorous and practical framework for conducting continuous, effective and reliable surveillance for safety endpoints. Finally, we will develop statistical approaches for incorporating prior information -- including demographic, epidemiologic or pharmacodynamic knowledge, for example -- to improve health effect estimation and inference when the health outcome of interest is rare and the statistical problem is thus difficult, as often occurs in safety surveillance. The ultimate goal of the proposed research is to enable biomedical researchers and public health regulators to carefully monitor and protect the health of the public by allowing them to more effectively and more reliably detect critical health effect signals that may be contained in population-scale EHR 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: 2023 ( Subtotal = $486,481 )
2023	2023	UNIVERSITY OF WASHINGTON	4333 BROOKLYN AVE NE	SEATTLE	WA	98195	KING	USA	Translation and Implementation Science Research for Heart, Lung, Blood Diseases, and Sleep Disorders	000	5	6/15/2023	NON-COMPETING CONTINUATION	$486,481
														Subtotal = $486,481

Issue Date FY: 2022 ( Subtotal = $486,320 )
2022	2022	UNIVERSITY OF WASHINGTON	4333 BROOKLYN AVE NE	SEATTLE	WA	98195	KING	USA	Translation and Implementation Science Research for Heart, Lung, Blood Diseases, and Sleep Disorders	000	4	6/14/2022	NON-COMPETING CONTINUATION	$486,320
														Subtotal = $486,320

Issue Date FY: 2021 ( Subtotal = $486,155 )
2021	2021	UNIVERSITY OF WASHINGTON	4333 BROOKLYN AVE NE	SEATTLE	WA	98195	KING	USA	Translation and Implementation Science Research for Heart, Lung, Blood Diseases, and Sleep Disorders	000	3	8/6/2021	NON-COMPETING CONTINUATION	$486,155
														Subtotal = $486,155

Issue Date FY: 2020 ( Subtotal = $485,987 )
2020	2020	UNIVERSITY OF WASHINGTON	4333 BROOKLYN AVE NE	SEATTLE	WA	98195	KING	USA	Translation and Implementation Science Research for Heart, Lung, Blood Diseases, and Sleep Disorders	000	2	6/30/2020	NON-COMPETING CONTINUATION	$485,987
														Subtotal = $485,987

Issue Date FY: 2019 ( Subtotal = $502,815 )
2019	2019	UNIVERSITY OF WASHINGTON	4333 BROOKLYN AVE NE	SEATTLE	WA	98195	KING	USA	Translation and Implementation Science Research for Heart, Lung, Blood Diseases, and Sleep Disorders	000	1	7/17/2019	NEW	$502,815
														Subtotal = $502,815

Grand Total All Awards = $2,447,758

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Statistical Methods for Incorporating Machine Learning Tools in Inference and Large-Scale Surveillance using Electronic Medical Records Data

Award Number: R01HL137808

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

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