CAREER: Information Extraction and Integration with Applications to Healthcare Question Answering

职业：信息提取和与医疗保健问答应用程序的集成

• 批准号: 2145202
• 负责人: Ndapandula Nakashole
• 金额: $ 60万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145202&HistoricalAwards=false
• 关键词: CAREER; Information; Extraction; Integration; Applications

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Getting a comprehensive answer to a medical question from Web search engines can be time-consuming because health information, as is common on the Web, is scattered across many websites. One difficulty is the prevalent vocabulary mismatch between different sources due to synonymous words, morphological variations, abbreviations, and different word orderings. Another challenge is that, for those who are not medical experts, health information can be complex and difficult to comprehend. Supportive visual representations can be helpful to various people, for example, those reading text not in their first language, older adults, or more generally, non-experts. To address these challenges, this project brings together health information in a single unified place by assimilating, synthesizing, and storing health information in a broad-coverage resource with a shared vocabulary. Such a resource serves the purpose of facilitating fast access to comprehensive answers to health questions to save people time who otherwise might need to spend a substantial amount of time reading different sources to connect the dots and get a complete answer to their information need. To help people understand complex health information, the project will generate summaries that combine text and supportive visualizations.This project will develop novel techniques for integrating information from disparate sources. This entails identifying relevant content and reconciling the mismatch in the vocabularies of different sources. To enforce a shared vocabulary across sources, the project will develop novel techniques for entity linking, that are not limited to recognizing entities seen at training time, as new diseases, treatments, other types of medical entities can emerge. For broad coverage, the project will consider content written by clinicians, researchers, and consumers. The project will convert this information into a graph structure that can be used to learn representations that further enhance coverage of the resource while maintaining high precision. The project will also develop novel techniques for multimodal summaries of healthcare answers to facilitate understanding of complex concepts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。从网络搜索引擎获得医疗问题的全面答案可能非常耗时，因为健康信息在网络上很常见，分散在许多网站上。一个困难是由于同义词、形态变化、缩写和不同的词序，不同来源之间普遍存在词汇不匹配。另一个挑战是，对于那些不是医学专家的人来说，健康信息可能很复杂，难以理解。支持性视觉表示对各种人都有帮助，例如，那些阅读非母语文本的人、老年人或更一般的非专家。为了应对这些挑战，该项目通过吸收、综合和存储具有共享词汇的广泛资源中的健康信息，将健康信息集中在一个统一的地方。这种资源的目的是促进快速获得对健康问题的全面答案，以节省人们的时间，否则他们可能需要花费大量的时间阅读不同的来源来连接点并获得对他们的信息需求的完整答案。为了帮助人们理解复杂的健康信息，该项目将生成结合联合收割机文本和支持性可视化的摘要。该项目将开发整合来自不同来源的信息的新技术。这需要确定相关内容，并协调不同来源词汇的不匹配。为了加强跨来源的共享词汇，该项目将开发用于实体链接的新技术，这些技术不仅限于识别在训练时看到的实体，因为新的疾病，治疗方法和其他类型的医疗实体可能会出现。为了广泛覆盖，该项目将考虑临床医生，研究人员和消费者撰写的内容。该项目将把这些信息转换成一个图形结构，可用于学习表示，进一步提高资源的覆盖率，同时保持高精度。该项目还将开发新的技术，用于医疗保健答案的多模态摘要，以促进对复杂概念的理解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

SYMPTOMIFY: Transforming Symptom Annotations with Language Model Knowledge Harvesting

SYMPTOMIFY：通过语言模型知识采集转换症状注释

• DOI: 10.18653/v1/2023.findings-emnlp.781
• 发表时间: 2023
• 期刊: Conference on Empirical Methods in Natural Language Processing
• 作者: Kim, Bosung;Nakashole, Ndapa
• 通讯作者: Nakashole, Ndapa

Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection

• DOI: 10.18653/v1/2022.bionlp-1.29
• 发表时间: 2022
• 期刊: Companion Proceedings of the ACM Web Conference 2023
• 作者: Bosung Kim;Ndapandula Nakashole

Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision

• DOI: 10.48550/arxiv.2209.15301
• 发表时间: 2022-09
• 期刊: ArXiv
• 作者: Khalil Mrini;Harpreet Singh;Franck Dernoncourt;Seunghyun Yoon;Trung Bui;Walter Chang;E. Farcas;Ndapandula Nakashole