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

快速而精细：用于近实时和细粒度过量监测的 NLP 方法

• 批准号: 10590000
• 负责人: Venkata Naga Ramakanth Kavuluru
• 金额: $ 134.47万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10590000
• 关键词: Accident and Emergency department; Address; Benchmarking; COVID-19 pandemic; Centers for Disease Control and Prevention (U.S.); Classification; Clinical; Code; Collaborations; Communities; Computer software; County; Data; Data Set; Death Certificates; Diagnosis; Docking; Drug usage; Emergency department visit; Emergency medical service; Epidemic; Evaluation; Event; Goals; Gold; Grain; Hand; Health; Helping to End Addiction Long-term; Human; Information Retrieval; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); Kentucky; Label; Lead; Learning; Letters; Life; Linear Models; Logistic Regressions; Machine Learning; Manuscripts; Methods; Modeling; Monitor; Naloxone; Names; Natural Language Processing; Neural Network Simulation; Non-linear Models; Opiate Addiction; Opioid; Overdose; Performance; Pharmaceutical Preparations; Prevention Research; Public Health; Records; Recurrence; Reporting; Research; Research Personnel; Resource Allocation; Resource-limited setting; Resources; Role; Semantics; Service delivery model; Signal Transduction; Site; Source; Speed; Spottings; Structure; Substance abuse problem; Supervision; System; Time; Training; Triage; United States National Institutes of Health; Universities; Update; Work; base; billing data; care delivery; dashboard; data modeling; data reuse; deep neural network; design; disability; experimental study; improved; injury prevention; insight; learning strategy; machine learning method; neural model; novel; open source; opioid misuse; opioid use disorder; overdose death; random forest; relating to nervous system; stem; supervised learning; syndromic surveillance; tool; transfer learning

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.

这项研究是 NIH 帮助消除成瘾长期 (HEAL) 计划的一部分，该计划旨在加快国家阿片类药物公共卫生危机的科学解决方案。 NIH HEAL 计划支持 NIH 的研究，以改善对阿片类药物滥用和成瘾的治疗。估计过量 (OD) 事件发生率是减轻持续 OD 流行病造成的损失的一个不可或缺的监测组成部分，快速更新非致命 OD 事件对于减少 OD 死亡的进一步升级至关重要。传统的 OD 监测方法目前依赖于 CDC 的综合征监测系统和汇总的急诊室 (ED) 计费数据，但国家级的估计受到严重延迟的困扰，因此，越来越多的人致力于监测（次）州级数据集。同时，人们认识到这些记录中叙述性数据的作用可以为 OD 和导致它们的药物提供补充信号，因为现有的基于 OD 定义的诊断代码的召回率较低。 （敏感性）。即使是在叙述中搜索术语的基于规则的定义也缺少叙述数据中的顺序语义上下文。为了解决这些缺点，我们建议设计和实现最先进的自然语言处理（NLP）。 ）使用深度神经网络（DNN）进行 OD 分类和药物术语细粒度识别从而得出 OD 的模型。为此，我们将首先创建并传播第一个模型。他们使用 ED 和 EMS 叙述的此类任务的公共黄金标准手工标记数据集将用于构建 DNN 模型，这些模型也将用于构建 DNN 模型。 向更广泛的 OD 监测社区公开分享，我们的模型有望大幅提高召回率，并及时实现更好的非致命 OD 监测。我们还将开发领域适应方法，以增强使用站点数据开发的模型的应用。总体而言，来自不同的网站。 我们的项目将为 OD 监控社区创建新颖的公共资源（数据、代码、模型），以利用 NLP 方法的最新进展。