Statistical NLP Analysis of Cross-discipline Clinical Text

跨学科临床文本的统计NLP分析

• 批准号: 6836781
• 负责人: JOHN F. HURDLE
• 金额: $ 9.45万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-26 至 2006-08-25
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6836781
• 关键词: bioinformatics; clinical research; computational biology; human data; library; mathematical model; public health; statistics /biometry

DESCRIPTION (provided by applicant): An emerging trend in computational linguistics is melding natural language processing (NLP) and machine learning (ML) to help computers make sense of human-generated free text. The blending of these disciplines is relatively rare in biomedical informatics. Past medical NLP/ML research work is biased heavily towards linguistic methods that attempt to reason about grammar and syntax aided by a domain-focal knowledge base (e.g., one for radiology or one for clinical pathology). The aim of the work proposed here takes a different tack: exploring the utility of a statistical approach to clinical NLP, one augmented by machine learning and concentrating on general progress notes from across multiple clinical domains. The specific clinical goal will be to identify adverse drug events described implicitly or explicitly in inpatient progress notes. Rather than relying on a narrow domain focus to provide enough context restriction to make text interpretation tractable, this approach will use statistical patterns in note author information (e.g., profession, note type, treating ward) and patient information (e.g., admit diagnosis, procedures performed, temporal note relationships) for context restriction. The research component of this proposal is divided into two categories: three small-scale projects designed to rapidly hone new skills developed under the training component, and a large-scale project that assesses the feasibility of cross-discipline clinical text analysis.

描述（由申请人提供）： 计算语言学的一个新兴趋势是融合自然语言处理（NLP）和机器学习（ML），以帮助计算机理解人类生成的自由文本。这些学科的融合在生物医学信息学中相对罕见。过去的医学NLP/ML研究工作严重偏向于语言学方法，这些方法试图通过领域焦点知识库（例如，一个用于放射学或一个用于临床病理学）。这里提出的工作的目的是采取不同的策略：探索临床NLP的统计方法的实用性，通过机器学习增强，并专注于多个临床领域的一般进展记录。具体的临床目标是识别住院患者病程记录中隐含或明确描述的药物不良事件。这种方法将使用笔记作者信息中的统计模式（例如，职业、笔记类型、治疗病房）和患者信息（例如，承认诊断、执行的程序、时间注释关系）用于上下文限制。该提案的研究部分分为两类：三个小规模项目，旨在快速磨练在培训部分下开发的新技能，以及一个评估跨学科临床文本分析可行性的大规模项目。