A review of automatic phenotyping approaches using electronic health records

Alzoubi, H., Alzubi, R., Ramzan, N., West, D., Al-Hadhrami, T. ORCID: 0000-0001-7441-604X and Alazab, M., 2019. A review of automatic phenotyping approaches using electronic health records. Electronics, 8 (11): 1235. ISSN 2079-9292

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Electronic Health Records (EHR) are a rich repository of valuable clinical information that exist in primary and secondary care databases. In order to utilize EHRs for medical observational research a range of algorithms for automatically identifying individuals with a specific phenotype have been developed. This review summarizes and offers a critical evaluation of the literature relating to studies conducted into the development of EHR phenotyping systems. This review describes phenotyping systems and techniques based on structured and unstructured EHR data. Articles published on PubMed and Google scholar between 2013 and 2017 have been reviewed, using search terms derived from Medical Subject Headings (MeSH). The popularity of using Natural Language Processing (NLP) techniques in extracting features from narrative text has increased. This increased attention is due to the availability of open source NLP algorithms, combined with accuracy improvement. In this review, Concept extraction is the most popular NLP technique since it has been used by more than 50% of the reviewed papers to extract features from EHR. High-throughput phenotyping systems using unsupervised machine learning techniques have gained more popularity due to their ability to efficiently and automatically extract a phenotype with minimal human effort.

Item Type: Journal article
Publication Title: Electronics
Creators: Alzoubi, H., Alzubi, R., Ramzan, N., West, D., Al-Hadhrami, T. and Alazab, M.
Publisher: MDPI
Date: 29 October 2019
Volume: 8
Number: 11
ISSN: 2079-9292
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Divisions: Schools > School of Science and Technology
Record created by: Jill Tomkinson
Date Added: 30 Oct 2019 11:57
Last Modified: 30 Oct 2019 11:57
URI: https://irep.ntu.ac.uk/id/eprint/38080

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