Extended Methods and Software Development for Health NLP

健康 NLP 的扩展方法和软件开发

基本信息

批准号：
10689709
负责人：
Steven Bethard
金额：
$ 44.67万
依托单位：
BOSTON CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-01-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10689709
关键词：
Address Adult Algorithms Apache Area Attention Big Data Childhood Clinical Communities Community Health Computer software Computerized Medical Record Data Data Set Detection Development Educational process of instructing Environment Evaluation Foundations Funding Goals Health Healthcare Information Retrieval Institution Investigation Joints Knowledge Knowledge Extraction Machine Learning Manuals Medical Medicine Methodology Methods Modeling Modernization Names Natural Language Processing Neural Network Simulation Ontology Patients Penetration Personal Satisfaction Phenotype Physicians Public Health Publications Research Risk Reduction Solid Source System Techniques Text Time Training Training and Infrastructure Uncertainty Unified Medical Language System Vision Work clinical training commercialization data streams deep learning deep learning algorithm design electronic data federated learning health data health knowledge insight learning strategy medical specialties method development multitask neural network novel online community open source point of care precision medicine programs social media software development structured data tool translational medicine

项目摘要

Project Summary Our program vision is to unravel the information buried in health-related narratives by advancing text-processing methods in a unified way across all the genres of health texts and distributing them through an advanced NLP software platform under solid governance and sustainability. The crosscutting theme is the investigation of methods for health NLP made possible by big data, fused with health knowledge. The underlying theme of this renewal is the development of methods towards generalizable, efficient and knowledge-rich models in the context of modern machine learning techniques, particularly models implementing attention mechanisms and using large unlabeled datasets. There is growing penetration of deep learning approaches in the field of health natural language processing. Our proposal aims to address critical methodological gaps and understudied areas in the current unprecedented fast-paced environment. Therefore, our renewal lays out novel and much needed explorations of health NLP research which we will advance through our specific aims. Our datasets will continue to span the spectrum of health-related data – Electronic Medical Records clinical narrative, patient-authored on- line community posts, and health-related social media. The evaluation of the methods we will develop will be performed on the key clinical tasks of concept extraction, relation extraction, and phenotyping with comparisons to other traditional or deep learning algorithms as baselines. We will demonstrate impact of our methods and tools through several use cases, ranging from clinical point of care to public health, to translational and precision medicine. Finally, we will disseminate our work through community activities to advance the state of the art in health natural language processing.

项目摘要我们的计划愿景是通过推进文本处理来揭示与健康相关的叙述中内置的信息以统一的方式在所有风格的健康文本中以统一的方式进行方法，并通过高级NLP分发它们在稳固的治理和可持续性下的软件平台。横切主题是调查健康NLP的方法使大数据成为可能，并与健康知识融合在一起。这个基本主题更新是开发用于概括，高效和知识丰富模型的方法现代机器学习技术的背景，尤其是实施注意机制的模型使用大型未标记的数据集。在健康领域，深度学习方法的渗透不断增长自然语言处理。我们的建议旨在解决关键的方法论差距和理解领域在当前空前的快节奏环境中。因此，我们的更新列出了小说，非常需要探索健康NLP研究，我们将通过我们的具体目标进行进步。我们的数据集将继续为了跨越与健康相关的数据范围 - 电子病历临床叙述，在患者身上实现线社区帖子和与健康相关的社交媒体。我们将开发的方法的评估将是与比较的概念提取，关系提取和表型的关键临床任务进行作为其他传统或深度学习算法作为基准。我们将证明我们的方法的影响通过几种用例的工具，从临床护理到公共卫生到翻译和精确药品。最后，我们将通过社区活动来传播我们的工作，以推动艺术的状态健康自然语言处理。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

"We make choices we think are going to save us": Debate and stance identification for online breast cancer CAM discussions.

“我们做出我们认为能够拯救我们的选择”：在线乳腺癌 CAM 讨论的辩论和立场识别。

DOI：
10.1145/3041021.3055134
发表时间：
2017
期刊：
Proceedings of the ... International World-Wide Web Conference. International WWW Conference
影响因子：
0
作者：
Zhang,Shaodian;Qiu,Lin;Chen,Frank;Zhang,Weinan;Yu,Yong;Elhadad,Noémie
通讯作者：
Elhadad,Noémie

UArizona at the CLEF eRisk 2017 Pilot Task: Linear and Recurrent Models for Early Depression Detection.

亚利桑那大学参加 CLEF eRisk 2017 试点任务：早期抑郁症检测的线性和循环模型。

DOI：
发表时间：
2017
期刊：
CEUR workshop proceedings
影响因子：
0
作者：
Sadeque,Farig;Xu,Dongfang;Bethard,Steven
通讯作者：
Bethard,Steven

Cataloguing Treatments Discussed and Used in Online Autism Communities.

对在线自闭症社区讨论和使用的治疗方法进行编目。

DOI：
10.1145/3038912.3052661
发表时间：
2017
期刊：
Proceedings of the ... International World-Wide Web Conference. International WWW Conference
影响因子：
0
作者：
Zhang,Shaodian;Kang,Tian;Qiu,Lin;Zhang,Weinan;Yu,Yong;Elhadad,Noémie
通讯作者：
Elhadad,Noémie

What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization.

DOI：
10.18653/v1/2021.naacl-main.382
发表时间：
2021-06
期刊：
Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting
影响因子：
0
作者：
Adams G;Alsentzer E;Ketenci M;Zucker J;Elhadad N
通讯作者：
Elhadad N

Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models.

中文临床笔记的推测检测：分词和嵌入模型的影响