Natural Language Processing and Machine Learning Techniques Help Achieve a Better Medical Practice

Natural Language Processing and Machine Learning Techniques Help Achieve a Better Medical Practice

Oana Frunza (University of Ottawa, Canada) and Diana Inkpen (University of Ottawa, Canada)
DOI: 10.4018/978-1-4666-1803-9.ch016
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Abstract

This book chapter presents several natural language processing (NLP) and machine learning (ML) techniques that can help achieve a better medical practice by means of extracting relevant medical information from the wealth of textual data. The chapter describes three major tasks: building intelligent tools that can help in the clinical decision making, tools that can automatically identify relevant medical information from the life-science literature, and tools that can extract semantic relations between medical concepts. Besides introducing and describing these tasks, methodological settings accompanied by representative results obtained on real-life data sets are presented.
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Background

From the wealth of relevant research that has been done in the biomedical domain, we are going to present only representative work focused on the tasks that we address. Research on clinical data is less represented in the literature, due to lack of access to data.

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