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Fast and fine: NLP methods for near real-time and fine-grained overdose surveillance

Award Number: R01DA057686
ORGANIZATION: NATIONAL INSTITUTE ON DRUG ABUSE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/30/2022
PERIOD OF PERFORMANCE END DATE: 09/29/2025

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Fast and fine: NLP methods for near real-time and fine-grained overdose surveillance - This study is part of the NIH’s Helping to End Addiction Long-term (HEAL) initiative to speed scientific solutions to the national opioid public health crisis. The NIH HEAL Initiative bolsters research across NIH to improve treatment for opioid misuse and addiction. Timely and accurate estimation of overdose (OD) event rates is an indispensable surveillance component to mit-igate the toll of the ongoing OD epidemic. Getting fast updates for nonfatal ODs is crucial in decreasing further escalations in OD deaths. Traditional approaches to OD surveillance currently rely on CDC's syndromic surveil-lance system and aggregated emergency department (ED) billing data. However national level estimates are plagued by substantial delays. Hence, there is an increasing push to monitor (sub)state level datasets including ED and emergency medical service (EMS) records. Meanwhile, the role of narrative data in these records is being recognized to offer complementary signal for ODs and drugs leading to them because existing diagnosis code based OD deﬁnitions are shown to have lower recall (sensitivity). Even rule-based deﬁnitions that search for terms in narratives are missing the sequential semantic context in narrative data. To address these shortcom-ings, we propose to design and implement state-of-the-art natural language processing (NLP) models using deep neural networks (DNNs) for OD classiﬁcation and ﬁne-grained recognition of drug terms leading to ODs. To this end, we will ﬁrst create and disseminate the ﬁrst of their kind public gold standard hand-labeled datasets for these tasks using ED and EMS narratives. Our de-identiﬁed notes will be used to build DNN models that will also be shared publicly to the wider OD surveillance community. Our models are expected to improve recall substantially and lead to better nonfatal OD surveillance in a timely manner. We will also develop domain adaption methods to enhance the application of models developed with data from a site to datasets from a different site. Overall, our project will create novel public resources (data, code, models) for the OD surveillance community to leverage latest advances in NLP methods.


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 = $0 )
2023	2022	UNIVERSITY OF KENTUCKY RESEARCH FOUNDATION, THE	500 S LIMESTONE 109 KINKEAD HALL	LEXINGTON	KY	40526	FAYETTE	USA	Drug Use and Addiction Research Programs	000	1	4/18/2023	NEW	$0
														Subtotal = $0

Issue Date FY: 2022 ( Subtotal = $1,344,685 )
2022	2022	UNIVERSITY OF KENTUCKY RESEARCH FOUNDATION, THE	500 S LIMESTONE 109 KINKEAD HALL	LEXINGTON	KY	40526	FAYETTE	USA	Drug Use and Addiction Research Programs	000	1	9/16/2022	NEW	$1,244,685
2022	2022	UNIVERSITY OF KENTUCKY RESEARCH FOUNDATION, THE	500 S LIMESTONE 109 KINKEAD HALL	LEXINGTON	KY	40526	FAYETTE	USA	Drug Use and Addiction Research Programs	001	1	9/26/2022	NEW	$100,000
														Subtotal = $1,344,685

Grand Total All Awards = $1,344,685

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Fast and fine: NLP methods for near real-time and fine-grained overdose surveillance

Award Number: R01DA057686

ORGANIZATION: NATIONAL INSTITUTE ON DRUG ABUSE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 09/30/2022

PERIOD OF PERFORMANCE END DATE: 09/29/2025

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