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
基本信息
- 批准号:10590000
- 负责人:
- 金额:$ 134.47万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:Accident and Emergency departmentAddressBenchmarkingCOVID-19 pandemicCenters for Disease Control and Prevention (U.S.)ClassificationClinicalCodeCollaborationsCommunitiesComputer softwareCountyDataData SetDeath CertificatesDiagnosisDockingDrug usageEmergency department visitEmergency medical serviceEpidemicEvaluationEventGoalsGoldGrainHandHealthHelping to End Addiction Long-termHumanInformation RetrievalInternational Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10)KentuckyLabelLeadLearningLettersLifeLinear ModelsLogistic RegressionsMachine LearningManuscriptsMethodsModelingMonitorNaloxoneNamesNatural Language ProcessingNeural Network SimulationNon-linear ModelsOpiate AddictionOpioidOverdosePerformancePharmaceutical PreparationsPrevention ResearchPublic HealthRecordsRecurrenceReportingResearchResearch PersonnelResource AllocationResource-limited settingResourcesRoleSemanticsService delivery modelSignal TransductionSiteSourceSpeedSpottingsStructureSubstance abuse problemSupervisionSystemTimeTrainingTriageUnited States National Institutes of HealthUniversitiesUpdateWorkbasebilling datacare deliverydashboarddata modelingdata reusedeep neural networkdesigndisabilityexperimental studyimprovedinjury preventioninsightlearning strategymachine learning methodneural modelnovelopen sourceopioid misuseopioid use disorderoverdose deathrandom forestrelating to nervous systemstemsupervised learningsyndromic surveillancetooltransfer learning
项目摘要
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.
这项研究是美国国立卫生研究院帮助结束长期成瘾(HEAL)倡议的一部分,该倡议旨在加速科学解决全国阿片类药物公共卫生危机。NIH HEAL倡议支持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(OD)事件发生率是减轻持续的OD流行病造成的损失不可或缺的监测组成部分。获得非致命性用药过量的快速更新对于减少用药过量死亡的进一步升级至关重要。传统的用药过量监测方法目前依赖于疾病预防控制中心的综合征监测系统和汇总急诊科(ED)计费数据。然而,国家层面的估计受到严重延误的困扰。因此,越来越多的人推动监测(次)州一级的数据集,包括急诊科和紧急医疗服务(EMS)记录。同时,由于现有的基于诊断代码的OD定义具有较低的召回率(灵敏度),这些记录中叙述性数据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。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。为了解决这些缺陷,我们建议设计和实现最先进的自然语言处理(NLP)模型,使用深度神经网络(dnn)进行OD分类和细粒度识别导致OD的药物术语。为此,我们将首先使用ED和EMS叙述为这些任务创建并传播第一个公共黄金标准手工标记数据集。我们去识别的笔记将被用来建立DNN模型,也将
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Venkata Naga Ramakanth Kavuluru其他文献
Venkata Naga Ramakanth Kavuluru的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Venkata Naga Ramakanth Kavuluru', 18)}}的其他基金
Advanced End-to-End Relation Extraction with Deep Neural Networks
使用深度神经网络进行高级端到端关系提取
- 批准号:
10386881 - 财政年份:2020
- 资助金额:
$ 134.47万 - 项目类别:
Advanced End-to-End Relation Extraction with Deep Neural Networks
使用深度神经网络进行高级端到端关系提取
- 批准号:
10200889 - 财政年份:2020
- 资助金额:
$ 134.47万 - 项目类别:
Advanced End-to-End Relation Extraction with Deep Neural Networks
使用深度神经网络进行高级端到端关系提取
- 批准号:
10615695 - 财政年份:2020
- 资助金额:
$ 134.47万 - 项目类别:
相似海外基金
Rational design of rapidly translatable, highly antigenic and novel recombinant immunogens to address deficiencies of current snakebite treatments
合理设计可快速翻译、高抗原性和新型重组免疫原,以解决当前蛇咬伤治疗的缺陷
- 批准号:
MR/S03398X/2 - 财政年份:2024
- 资助金额:
$ 134.47万 - 项目类别:
Fellowship
CAREER: FEAST (Food Ecosystems And circularity for Sustainable Transformation) framework to address Hidden Hunger
职业:FEAST(食品生态系统和可持续转型循环)框架解决隐性饥饿
- 批准号:
2338423 - 财政年份:2024
- 资助金额:
$ 134.47万 - 项目类别:
Continuing Grant
Re-thinking drug nanocrystals as highly loaded vectors to address key unmet therapeutic challenges
重新思考药物纳米晶体作为高负载载体以解决关键的未满足的治疗挑战
- 批准号:
EP/Y001486/1 - 财政年份:2024
- 资助金额:
$ 134.47万 - 项目类别:
Research Grant
Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用
- 批准号:
MR/X03657X/1 - 财政年份:2024
- 资助金额:
$ 134.47万 - 项目类别:
Fellowship
CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds
CRII:SHF:一种用于虚拟化云的新型地址转换架构
- 批准号:
2348066 - 财政年份:2024
- 资助金额:
$ 134.47万 - 项目类别:
Standard Grant
The Abundance Project: Enhancing Cultural & Green Inclusion in Social Prescribing in Southwest London to Address Ethnic Inequalities in Mental Health
丰富项目:增强文化
- 批准号:
AH/Z505481/1 - 财政年份:2024
- 资助金额:
$ 134.47万 - 项目类别:
Research Grant
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
- 批准号:
10107647 - 财政年份:2024
- 资助金额:
$ 134.47万 - 项目类别:
EU-Funded
BIORETS: Convergence Research Experiences for Teachers in Synthetic and Systems Biology to Address Challenges in Food, Health, Energy, and Environment
BIORETS:合成和系统生物学教师的融合研究经验,以应对食品、健康、能源和环境方面的挑战
- 批准号:
2341402 - 财政年份:2024
- 资助金额:
$ 134.47万 - 项目类别:
Standard Grant
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
- 批准号:
10106221 - 财政年份:2024
- 资助金额:
$ 134.47万 - 项目类别:
EU-Funded
Recite: Building Research by Communities to Address Inequities through Expression
背诵:社区开展研究,通过表达解决不平等问题
- 批准号:
AH/Z505341/1 - 财政年份:2024
- 资助金额:
$ 134.47万 - 项目类别:
Research Grant