Multi-source clinical Question Answering system

多源临床问答系统

• 批准号: 7936991
• 负责人: GUERGANA K. SAVOVA
• 金额: $ 49.14万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7936991
• 关键词: Address; Algorithms; Area; Artificial Intelligence; Caring; Clinic; Clinical; Clinical Data; Clinical Research; Clinical Trials; Colorado; Complement; Complex; Consumption; Data Sources; Databases; Diagnosis; Electronics; Environment; Event; Health; Information Retrieval; Knowledge; Knowledge Extraction; Linguistics; Machine Learning; Medical; Paper; Patient Care; Patients; Physician' s Role; Physicians; Play; Practice Guidelines; Protocols documentation; PubMed; Publishing; Recording of previous events; Research; Research Personnel; Resources; Retrieval; Semantics; Signal Transduction; Solutions; Source; Structure; Syndrome; System; Technology; Testing; Text; Training; Translational Research; Universities; Work; abstracting; base; clinically relevant; health care delivery; health care quality; health record; improved; innovation; insight; interest; language processing; point of care; response; semantic processing; tool; user-friendly

DESCRIPTION (provided by applicant): / Abstract (Limit: 1 page) Our proposal addresses the following challenge area: 06-LM-101* Intelligent Search Tool for Answering Clinical Questions. Develop new computational approaches to information retrieval that would allow a clinician or clinical researcher to pose a single query that would result in search of multiple data sources to produce a coherent response that highlights key relevant information which may signal new insights for clinical research or patient care. Information that could help a clinician diagnose or manage a health condition, or help a clinical researcher explore the significance of issues that arise during a clinical trial, is scattered across many different types of resources, such as paper or electronic charts, trial protocols, published biomedical articles, or best-practice guidelines for care. Develop artificial intelligence and information retrieval approaches that allow a clinician or researcher confronting complex patient problems to pose a single query that will result in a search that appears to "understand" the question, a search that inspects multiple databases and brings findings together into a useful answer. Clinical question answering (cQA) systems focus on the physician needs usually at the point of care, or the investigator in the lab. The questions usually asked either require information highly specific to their patient, e.g. the patient's lab results or previous history, answered by the patient's health record, or a more general type of information usually answered through generally available information sources. QA systems enhance the results of search engines by providing a concise summary of relevant information along with source hits. PubMed (http://www.ncbi.nlm.nih.gov/pubmed/) is the most ubiquitous biomedical search engine, however because it is a search engine the information retrieved is based on keyword searches and is not presented in a form for immediate consumption; the user has to drill down into the content of the webpages to find the facts/statements of interest. Moreover, the information that the clinician needs is likely to be of different types, for example a definition of a syndrome in combination with specific actions triggered by a particular diagnosis for a particular patient. Such information resides in different sources - encyclopedic and the EMR - and has to be dynamically accessed and presented to the user in an easily digestible format. We propose to develop a unified platform for clinical QA from multiple sources of clinical and biomedical narrative that implements semantic processing of the questions by fusing two existing technologies - the Mayo clinical Text Analysis and Knowledge Extraction System and the University of Colorado's Question Answering System. The specific research questions we are aiming to answer are: "How much effort is required to port a general semantic QA system to the clinical domain? How much additional domain-specific training is required? "What is the accuracy of such a system? Question Answering in the clinical domain is an emerging area of research. The challenges in the field are mainly attributed to the number of components that require domain specific training along with strict system requirements in terms of high precision and recall complemented by an accessible and user-friendly presentation. Our approach to overcome them is to re-use components already in place as part of Mayo clinical Text Analysis and Knowledge Extraction System and the University of Colorado's Question Answering System. Our approach is innovative in bringing together information from encyclopedic sources and the EMR to present it into a unified form to the clinician at the point of care or the investigator in the lab. The technology for that is based on semantic language processing which aims at "understanding" the meaning of the question and the narrative. Our proposed system holds the potential to impact quality of healthcare and translational research. Our approach is feasible because it uses content already in the EMR at the Mayo Clinic along with general medical knowledge from multiple readily-available resources. The proposed system will be built off mature and tested components allowing a fast and robust delivery cycle. Our unique integration of technologies together with sophisticated statistical machine learning algorithms applied to rich linguistic knowledge about events, contradictions, semantic structure, and question-types, will allow us to build a system which significantly extends the range of possible question types and responses available to clinicians, and seamlessly fuses these to generate a response. Our proposed work represents a high impact area that has the potential to improve healthcare delivery because it addresses needs that have been well-documented and studied (Ely et al., 2005). We aim to provide a unified multi-source solution for semantic retrieval, access and summarization of relevant information at the point of care or the lab. As such, the proposed cQA has the potential to play a vital and important decision- support role for the physician or the biomedical investigator. (max 2-3 sentences) Clinical question answering (cQA) systems focus on the physician needs usually at the point of care, or the investigator in the lab. The questions usually asked either require information highly specific to their patient, e.g. the patient's lab results or previous history, answered by the patient's health record, or a more general type of information usually answered through generally available information sources. Our proposed work to provide a unified multi-source solution for semantic retrieval, access and summarization of relevant information at the point of care or the lab, represents a high impact area that has the potential to improve healthcare delivery because it addresses needs that have been well-documented and studied.

描述（由申请人提供）： /摘要（限制：1页）我们的提案解决了以下挑战领域：06-LM-101* 用于存储临床问题的智能搜索工具。开发新的信息检索计算方法，使临床医生或临床研究人员能够提出一个单一的查询，这将导致搜索多个数据源，以产生一个连贯的响应，突出显示关键的相关信息，这可能标志着临床研究或患者护理的新见解。可以帮助临床医生诊断或管理健康状况，或帮助临床研究人员探索临床试验期间出现的问题的重要性的信息分散在许多不同类型的资源中，例如纸质或电子图表，试验方案，已发表的生物医学文章或最佳实践指南。开发人工智能和信息检索方法，允许临床医生或研究人员面对复杂的患者问题提出一个单一的查询，这将导致一个似乎“理解”问题的搜索，一个检查多个数据库并将发现汇集到一起的搜索，成为一个有用的答案。临床问题回答（cQA）系统通常专注于护理点的医生需求或实验室的研究者。通常询问的问题要么需要高度特定于其患者的信息，例如患者的实验室结果或先前的病史，由患者的健康记录回答，要么通常通过一般可用的信息源回答的更一般类型的信息。QA系统通过提供相关信息的简明摘要沿着源命中来增强搜索引擎的结果。PubMed（http：//www.ncbi.nlm.nih.gov/pubmed/）是最普遍的生物医学搜索引擎，但由于它是一个搜索引擎，检索到的信息是基于关键词搜索，而不是以立即消费的形式呈现;用户必须深入网页的内容，以找到感兴趣的事实/陈述。此外，临床医生需要的信息可能是不同类型的，例如综合征的定义与由特定患者的特定诊断触发的特定动作相结合。这些信息存在于不同的来源-电子病历和电子病历-必须动态访问，并以易于理解的格式呈现给用户。我们建议开发一个统一的平台，临床QA从多个来源的临床和生物医学的叙述，实现语义处理的问题，融合现有的两种技术-马约临床文本分析和知识提取系统和科罗拉多大学的提问系统。具体的研究问题，我们的目标是回答：“需要多少努力，以港口的一般语义QA系统的临床领域？需要多少额外的特定领域培训？“这种系统的准确性如何？临床领域的问题检索是一个新兴的研究领域。该领域的挑战主要是由于需要特定领域培训的组件数量沿着在高精度和召回方面的严格系统要求，以及可访问和用户友好的演示文稿。我们克服这些问题的方法是重新使用已经存在的组件，作为马约临床文本分析和知识提取系统以及科罗拉多大学问题检索系统的一部分。我们的方法是创新的，将来自儿科来源的信息和EMR结合在一起，以统一的形式呈现给护理点的临床医生或实验室的研究人员。该技术基于语义语言处理，旨在“理解”问题和叙述的含义。我们提出的系统有可能影响医疗保健和转化研究的质量。我们的方法是可行的，因为它使用的内容已经在电子病历在马约诊所沿着一般医学知识，从多个现成的资源。拟议的系统将建立在成熟和测试的组件，允许快速和强大的交付周期。我们独特的技术集成以及应用于事件，矛盾，语义结构和问题类型的丰富语言知识的复杂统计机器学习算法，将使我们能够构建一个系统，显着扩展临床医生可用的可能问题类型和响应的范围，并无缝融合这些以生成响应。我们提出的工作代表了一个具有高影响力的领域，该领域有可能改善医疗保健服务，因为它解决了已被充分记录和研究的需求（伊利等人，2005年）。我们的目标是提供一个统一的多源解决方案，用于在护理点或实验室进行语义检索，访问和总结相关信息。因此，拟议的cQA有可能为医生或生物医学研究者发挥至关重要的决策支持作用。(max 2-3句话）临床问答（cQA）系统关注的是通常在护理点的医生需求，或实验室中的研究者需求。通常询问的问题要么需要高度特定于其患者的信息，例如患者的实验室结果或先前的病史，由患者的健康记录回答，要么通常通过一般可用的信息源回答的更一般类型的信息。我们提出的工作是提供一个统一的多源解决方案，用于在护理点或实验室进行语义检索、访问和总结相关信息，这是一个具有高影响力的领域，有可能改善医疗保健服务，因为它解决了已经有充分记录和研究的需求。

项目成果

The MiPACQ clinical question answering system.

• 发表时间: 2011
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: B. Cairns;Rodney D. Nielsen;James J. Masanz;James H. Martin;M. Palmer;Wayne H. Ward;G. Savova