Statistical NLP Analysis of Cross-discipline Clinical Text

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。在生物医学信息学中，这些学科的融合相对较少。过去的医学NLP/ML研究工作严重偏向于语言方法，这些方法试图借助领域焦点知识库(例如，放射学或临床病理学的知识库)来推理语法和句法。这里提出的工作的目的采取了不同的策略：探索统计方法对临床NLP的效用，一种由机器学习增强的方法，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患者进度记录中隐含或明确描述的不良药物事件。这种方法将使用笔记作者信息(例如，职业、笔记类型、治疗病房)和患者信息(例如，入院诊断、执行的程序、时间笔记关系)中的统计模式来进行上下文约束，而不是依赖于狭窄的领域焦点来提供足够的上下文限制来使文本解释变得容易处理。该提案的研究部分分为两类：三个旨在快速磨练在培训部分下开发的新技能的小规模项目，以及一个评估跨学科临床文本分析可行性的大型项目。

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.