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

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学问题的全面答案可能很耗时，因为健康信息分散在许多网站上，这在网络上很常见。一个困难是由于同义词、形态变化、缩写和不同的词序导致不同来源之间普遍存在的词汇不匹配。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。支持性的视觉表现对不同的人都有帮助，例如，那些阅读非母语文本的人，老年人，或者更普遍的，非专家。为了应对这些挑战，该项目通过在具有共享词汇表的广泛覆盖资源中吸收、综合和存储健康信息，将健康信息汇集到一个统一的地方。这种资源的目的是促进快速获得健康问题的全面答案，从而节省人们的时间，否则他们可能需要花费大量时间阅读不同的来源，以便将要点联系起来，并获得他们所需信息的完整答案。为了帮助人们理解复杂的健康信息，该项目将生成结合文本和支持性可视化的摘要。该项目将开发用于集成来自不同来源的信息的新技术。这需要识别相关内容并协调不同来源的词汇表中的不匹配。为了实现跨来源的共享词汇表，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培训时看到的实体，还可能出现新的疾病、治疗方法和其他类型的医疗实体。为了覆盖范围更广，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，可以用来学习表征，进一步增强资源的覆盖范围，同时保持高精度。该项目还将开发用于医疗保健答案多模式摘要的新技术，以促进对复杂概念的理解。该奖项反映了美国国家科学基金会的法定使命，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。

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.