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Open AccessFeature PaperArticle

A Review of Automatic Phenotyping Approaches using Electronic Health Records

School of Computer and Information Technology, Jordan University of Science and Technology, Irbid 22110, Jordan
Department of Computer Science, Faculty of Information Technology, Middle East University, Amman 11831, Jordan
School of Engineering and Computing, University of the West of Scotland, Paisley PA1 2BE, UK
School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
College of Engineering, IT and Environment, Charles Darwin University, Darwin 0815, Australia
Authors to whom correspondence should be addressed.
Electronics 2019, 8(11), 1235;
Received: 27 September 2019 / Revised: 21 October 2019 / Accepted: 22 October 2019 / Published: 29 October 2019
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. View Full-Text
Keywords: electronic health records; phenotyping; natural language processing; machine learning; rule-based electronic health records; phenotyping; natural language processing; machine learning; rule-based
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Alzoubi, H.; Alzubi, R.; Ramzan, N.; West, D.; Al-Hadhrami, T.; Alazab, M. A Review of Automatic Phenotyping Approaches using Electronic Health Records. Electronics 2019, 8, 1235.

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