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Statistical NLP Analysis of Cross-discipline Clinical Text

跨学科临床文本的统计NLP分析

基本信息

批准号：
6944955
负责人：
JOHN F. HURDLE
金额：
$ 3.88万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2004
资助国家：
美国
起止时间：
2004-08-26 至 2007-05-30
项目状态：
已结题

项目摘要

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 inforrnatics. 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.

计算语言学的新兴趋势是自然语言处理(NLP)和机器的融合学习(ML)，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是相对罕见的。过去的医学NLP/ML研究工作严重偏向一种语言方法，试图借助某一领域的知识对语法和句法进行推理基础(例如，一个放射学或一个临床病理学)。这里提出的工作目标需要一个不同的策略：探索统计方法在临床NLP中的应用，一种由机器增强的方法学习并专注于来自多个临床领域的一般进展笔记。具体的临床目标将是确定住院过程中隐含或明确描述的不良药物事件笔记。而不是依赖于狭隘的领域焦点来提供足够的上下文限制来生成文本解释容易处理，该方法将使用笔记作者信息(例如，职业，病历类型、治疗病房)和患者信息(例如，入院诊断、已执行的手术、临时病历关系)用于上下文限制。这项建议的研究部分分为两类：三个小规模项目，旨在迅速磨练在培训部分下开发的新技能；以及评估跨学科临床文本分析可行性的大型项目。