CLINICAL TEXT MINING
3,425.00₹ 4,644.00₹
- Author: HERCULES DALIANIS
- ISBN: 9783030087159
- Availability: In Stock
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ABOUT THE BOOK
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.