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

> **NIH NIH R01** · UNIVERSITY OF KENTUCKY · 2022 · $1,344,685

## Abstract

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.

## Key facts

- **NIH application ID:** 10590000
- **Project number:** 1R01DA057686-01
- **Recipient organization:** UNIVERSITY OF KENTUCKY
- **Principal Investigator:** Venkata Naga Ramakanth Kavuluru
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,344,685
- **Award type:** 1
- **Project period:** 2022-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10590000

## Citation

> US National Institutes of Health, RePORTER application 10590000, Fast and fine: NLP methods for near real-time and fine-grained overdose surveillance (1R01DA057686-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10590000. Licensed CC0.

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