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Developing Suicide Risk Algorithms for Diverse Clinical Settings using Data Fusion

Award Number: R01MH124740
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/16/2020
PERIOD OF PERFORMANCE END DATE: 06/30/2025

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Developing Suicide Risk Algorithms for Diverse Clinical Settings using Data Fusion - Suicide is one of the most serious public health problems facing the United States. Recent evidence indicates that many if not most of individuals who die by suicide have been in contact with the healthcare system in the months prior to their death, providing data that can be used to identify patients at risk prior to an attempt. The proposed project will develop an innovative method for identifying patients at risk of suicidal behavior using data from a large multistate health information exchange, integrated with data from the State of Connecticut’s CHIME hospital database. We will develop and test suicide risk algorithms using principles associated with transfer learning, in which information from a comprehensive external data source is used to improve prediction in a more limited dataset. Specifically, we will use multimodal data fusion techniques to develop and test algorithms that can identify patients at risk of suicidal behavior by clinicians in hospitals with limited numbers of patients, select patient populations, and lack of access to outpatient data. This approach is not only generalizable to hospitals throughout the US but can be extended to very diverse clinical settings, e.g., primary and specialty care practices, community health centers, urgent care clinics. The potential public health significance of this study is substantial. The fragmentation of the healthcare system, particularly in relation to patients’ behavioral health needs, highlights the critical need to cultivate comprehensive, system-wide approaches to identifying and managing at patients at risk of suicide.


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 = -$13,342 )
2026	2023	UNIVERSITY OF CONNECTICUT HEALTH CENTER	263 FARMINGTON AVE	FARMINGTON	CT	06030	CAPITOL	USA	Mental Health Research Grants	000	4	11/21/2025	NON-COMPETING CONTINUATION	-$13,342
														Subtotal = -$13,342

Issue Date FY: 2023 ( Subtotal = $750,817 )
2023	2023	UNIVERSITY OF CONNECTICUT HEALTH CENTER	263 FARMINGTON AVE	FARMINGTON	CT	06030	CAPITOL	USA	Mental Health Research Grants	000	4	5/29/2023	NON-COMPETING CONTINUATION	$750,817
														Subtotal = $750,817

Issue Date FY: 2022 ( Subtotal = $778,997 )
2022	2022	UNIVERSITY OF CONNECTICUT	263 FARMINGTON AVE, ASB #3	FARMINGTON	CT	06032	CAPITOL	USA	Mental Health Research Grants	000	3	6/16/2022	NON-COMPETING CONTINUATION	$778,997
														Subtotal = $778,997

Issue Date FY: 2021 ( Subtotal = $798,717 )
2021	2021	UNIVERSITY OF CONNECTICUT	263 FARMINGTON AVE, ASB #3	FARMINGTON	CT	06032	CAPITOL	USA	Mental Health Research Grants	001	2	5/28/2021	NON-COMPETING CONTINUATION	$798,717
2021	2020	UNIVERSITY OF CONNECTICUT	263 FARMINGTON AVE, ASB #3	FARMINGTON	CT	06032	CAPITOL	USA	Mental Health Research Grants	000	1	11/18/2020	NEW	$0
														Subtotal = $798,717

Issue Date FY: 2020 ( Subtotal = $854,246 )
2020	2020	UNIVERSITY OF CONNECTICUT	263 FARMINGTON AVE, ASB #3	FARMINGTON	CT	06032	CAPITOL	USA	Mental Health Research Grants	000	1	9/16/2020	NEW	$854,246
														Subtotal = $854,246

Grand Total All Awards = $3,169,435

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Developing Suicide Risk Algorithms for Diverse Clinical Settings using Data Fusion

Award Number: R01MH124740

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 09/16/2020

PERIOD OF PERFORMANCE END DATE: 06/30/2025

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