Clinical Text Mining: Secondary Use of Electronic Patient Records

This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records.

It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters.

The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.

這本開放存取的書籍描述了將自然語言處理和機器學習方法應用於電子病歷中的臨床文本的結果。

書籍分為十二章。第1至4章討論了原始紙質病歷的歷史和背景，它們的目的以及它們的撰寫和結構。這些初步章節不需要任何技術或醫學背景知識。其餘八章更具技術性，描述了各種醫學分類和術語，如ICD診斷代碼、SNOMED CT、MeSH、UMLS和ATC。第5至10章介紹了自然語言處理和信息檢索的基本工具，以及如何將它們應用於臨床文本。書中還解釋了基於規則和基於機器學習的方法之間的區別，以及監督和非監督機器學習方法之間的區別。接下來，討論了使用敏感病人記錄進行研究的道德問題，包括去識別化電子病歷和安全存儲病人記錄的方法。書的結尾章節介紹了臨床文本挖掘的一些應用，並總結了前面章節的經驗教訓。

這本書全面概述了臨床文本挖掘中出現的技術問題，並為健康信息學、計算語言學和信息檢索的高級學生以及進入這些領域的研究人員提供了寶貴的指南。