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WIREs Data Mining Knowl Discov
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An advanced review on text mining in medicine

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Health care professionals produce abundant textual information in their daily clinical practice and this information is stored in many diverse sources and, generally, in textual form. The extraction of insights from all the gathered information, mainly unstructured and lacking normalization, is one of the major challenges in computational medicine. In this respect, text mining (TM) assembles different techniques to derive valuable insights from unstructured textual data so it has led to be especially relevant in medicine. The aim of this paper is therefore to provide an extensive review of existing techniques and resources to perform TM tasks in medicine. In this review, more than 90 relevant research studies have been analyzed, describing the most important practical applications, terminological resources, tools, and open challenges of TM in medicine. This article is categorized under: Application Areas > Health Care Algorithmic Development > Biological Data Mining Algorithmic Development > Hierarchies and Trees Algorithmic Development > Ensemble Methods
Text mining process
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Semantic MEDLINE graph. Example of summarization according to treatment of a disease summary type (query: Alzheimer's disease). The nodes represent different entities found in citations and the arrows the connections between those entities
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Number of scientific publications returned by PubMed and Embase biomedical databases (keywords: text mining. Last updated June 2018)
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Browse by Topic

Algorithmic Development > Biological Data Mining
Algorithmic Development > Hierarchies and Trees
Algorithmic Development > Ensemble Methods
Application Areas > Health Care

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