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

这项研究是NIH帮助结束成瘾长期(Hear)倡议的一部分，该倡议旨在加快科学解决国家阿片类药物公共健康危机的速度。NIH Hear Initiative支持整个NIH的研究，以改善阿片类药物滥用和成瘾的治疗。及时和准确地估计过量服药(OD)事件发生率是麻省理工学院掌握正在进行的服药过量流行造成的死亡人数不可或缺的监测组成部分。获得非致命性服药的快速更新对于减少服药过量死亡的进一步上升至关重要。传统的药物过量监测方法目前依赖于疾控中心的症状监测系统和汇总的急诊科(ED)账单数据。然而，国家一级的估计数受到大量延误的困扰。因此，对包括急救和紧急医疗服务(EMS)记录在内的(子)州级别数据集的监控的推动力度越来越大。同时，叙述性数据在这些记录中的作用正在被认识到为OD和导致它们的药物提供补充信号，因为现有的基于诊断代码的OD defi信息被证明具有较低的召回率(敏感性)。即使是在叙事中搜索术语的基于规则的Definitions也缺少叙事数据中的连续语义上下文。为了解决这些不足，我们建议设计和实现最先进的自然语言处理模型，使用深度神经网络(DNN)来进行OD分类fi阳离子和导致OD的药物术语的fi粒度识别。为此，我们将使用ED和fi叙事为这些任务创建和传播fiRst这类公共黄金标准手工标记数据集。我们的de-identified备注将用于构建DNN模型，该模型也将 公开分享给更广泛的OD监控社区。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性药物过量监测。我们还将开发领域适应方法，以增强使用来自一个站点的数据开发的模型到来自不同站点的数据集的应用。总的来说， 我们的项目将为OD监测社区创建新的公共资源(数据、代码、模型)，以利用NLP方法的最新进展。

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.