HERMES - Help physicians to Extract and aRticulate Multimedia information from li

HERMES - 帮助医生从李中提取和阐明多媒体信息

• 批准号: 7690941
• 负责人: HONG YU
• 金额: $ 35.15万
• 依托单位: UNIVERSITY OF WISCONSIN MILWAUKEE
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7690941
• 关键词: Area; Back; Cardiovascular system; Case Study; Clinical; Clinical Research; Communities; Databases; Diagnostic Procedure; Edema; Erythema; Evaluation Studies; Evidence Based Medicine; Generic Drugs; Health Personnel; Hospitals; Hour; Image; Information Retrieval; Information Retrieval Systems; Information Systems; Journals; Knowledge; Literature; Machine Learning; Maps; Measures; Medical; Medical Imaging; Medicine; Modeling; Multimedia; New England; Pain; Patient Care; Patients; Physicians; Primary Care Physician; PubMed; Publishing; Redness; Reporting; Resources; Review Literature; Semantics; System; Text; Time; Toes; Ultrasonography; Visit; Work; Writing; clinical practice; design; foot; journal article; language processing; meetings; older men; patient oriented; response; usability

DESCRIPTION (provided by applicant): Physicians have many questions when seeing patients. Primary care physicians are reported to generate between 0.7 and 18.5 questions for every 10 patient visits. The published medical literature is an important resource helping physicians to access up-to-date clinical information and thereby to enhance the quality of patient care. For example, the case study in the above example (i.e., diagnostic procedures and treatment for cellulites) was published in a "Clinical Practice" article in the New England Journal of Medicine (NEJM). Although PubMed is frequently used by physicians in large hospitals, it does not return answers to specific questions. Frequently, PubMed returns a large number of articles in response to a specific user query. Physicians have limited time for browsing the articles retrieved; it has been found that physicians spend on average two minutes or less seeking an answer to a question, and that if a search takes longer it is likely to be abandoned. An evaluation study has shown that it takes an average of more than 30 minutes for a healthcare provider to search for answer from PubMed, which makes "information seeking ... practical only `after hours' and not in the clinical setting." It has been concluded that a lack of time is the most common obstacle resulting in many unanswered medical questions. The importance of answering physicians' questions at the point of patient care has been widely recognized by the medical community. Many medical databases (e.g., UpToDate and Thomson MICROMEDEX) provide summaries to answer important medical questions related to patient care. However, most of the summaries are written by medical experts who manually review the literature information. The databases are limited in their scope and timeliness. We hypothesize that we can develop medical language processing (MLP) approaches to build a fully automated system HERMES - Help physicians to Extract and aRticulate Multimedia information from literature to answer their ad-hoc medical quEstionS. HERMES will automatically retrieve, extract, analyze, and integrate text, image, and video from the literature and formulate them as answers to ad-hoc medical questions posed by physicians. Our preliminary results show that even a limited HERMES working system outperformed other information retrieval systems and can generate answers within a timeframe necessary to meet the demands of physicians. HERMES promise to assist physicians for practicing evidence-based medicine (EBM), the medical practice that involves the explicit use of current best evidence, i.e., high-quality patient-centered clinical research reported in the primary medical literature. Our specific aims are: 1) Identify information needs from ad-hoc medical questions. We will incorporate rich semantic, statistical, and machine learning approaches to map ad-hoc medical questions to their component question types automatically. A component question type is a generic, simple question type that requires an answer strategy that is different from other component question types. 2) Develop new information retrieval models that integrate domain-specific knowledge for retrieving relevant documents in response to an ad-hoc medical question. 3) Extract relevant text, images, and videos from the retrieved documents in response to an ad-hoc medical question. 4) Integrate text, images, and videos, fusing information to generate a short and coherent multimedia summary. 5) Design a usability study to measure efficacy, accuracy and perceived ease of use of HERMES and to compare HERMES with other information systems.

描述（由申请人提供）：医生在看病人时有很多问题。据报道，初级保健医生每10次患者就诊就会产生0.7至18.5个问题。已发表的医学文献是帮助医生获得最新临床信息的重要资源，从而提高患者护理质量。例如，上述示例中的案例研究（即，蜂窝织炎的诊断程序和治疗）发表在新英格兰医学杂志（NEJM）的“临床实践”文章中。虽然PubMed经常被大医院的医生使用，但它不返回特定问题的答案。通常，PubMed会返回大量文章来响应特定的用户查询。医生浏览检索到的文章的时间有限;已经发现，医生平均花费两分钟或更少的时间来寻找问题的答案，如果搜索时间更长，则可能会被放弃。一项评估研究表明，医疗保健提供者从PubMed搜索答案平均需要30多分钟，这使得“信息寻求......仅在“下班后”才实用，在临床环境中不实用。“已经得出结论，缺乏时间是导致许多未回答的医疗问题的最常见障碍。 回答医生在病人护理方面的问题的重要性已被医学界广泛认可。许多医学数据库（例如，UpToDate和Thomson MICROMEDEX）提供摘要以回答与患者护理相关的重要医学问题。然而，大多数总结都是由手动审查文献信息的医学专家撰写的。这些数据库的范围和及时性有限。 我们假设，我们可以开发医学语言处理（MLP）的方法，建立一个完全自动化的系统爱马仕-帮助医生提取和分类多媒体信息从文献中回答他们的特设医疗问题。爱马仕将自动检索、提取、分析和整合文献中的文本、图像和视频，并将其作为医生提出的特殊医学问题的答案。我们的初步研究结果表明，即使是有限的爱马仕工作系统优于其他信息检索系统，可以在必要的时间内生成答案，以满足医生的需求。爱马仕承诺协助医生实践循证医学（EBM），即明确使用当前最佳证据的医疗实践，即，主要医学文献中报告的高质量以患者为中心的临床研究。 我们的具体目标是： 1)从特定的医学问题中识别信息需求。我们将采用丰富的语义、统计和机器学习方法，自动将临时医疗问题映射到其组成问题类型。组件问题类型是一种通用的简单问题类型，它需要与其他组件问题类型不同的回答策略。 2)开发新的信息检索模型，集成特定领域的知识，检索相关文档，以响应特定的医学问题。 3)从检索到的文档中提取相关文本、图像和视频，以响应特定的医疗问题。 4)整合文本、图像和视频，融合信息以生成简短连贯的多媒体摘要。 5)设计一项可用性研究，以衡量爱马仕的有效性、准确性和易用性，并将爱马仕与其他信息系统进行比较。