Statistical NLP Analysis of Cross-discipline Clinical Text

计算语言学的一个新趋势是将自然语言处理（NLP）和机器语言相结合 机器学习（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相对罕见。过去的医学NLP/ML研究工作严重偏向于 一种语言学方法，试图借助领域焦点知识来推理语法和句法 基底（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需要一个 不同的策略：探索临床NLP的统计方法的实用性，一种由机器增强的方法 学习并专注于多个临床领域的一般进展记录。具体 临床目标是识别住院患者进展中隐含或明确描述的药物不良事件 notes.而不是依赖于一个狭窄的领域焦点，以提供足够的上下文限制，使文本 解释易处理，该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，进行的程序，时间记录 关系）的上下文限制。本提案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， 一个大型项目，评估跨学科临床文本分析的可行性。

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.