A Review of Automatic Phenotyping Approaches using Electronic Health Records

Hadeel Alzoubi, Raid Alzubi, Naeem Ramzan, Daune West, Tawfik Al-Hadhrami, Mamoun Alazab

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Abstract

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.
Original languageEnglish
Article number1235
Pages (from-to)1-23
Number of pages23
JournalElectronics
Volume8
Issue number11
DOIs
Publication statusPublished - 29 Oct 2019

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Alzoubi, H., Alzubi, R., Ramzan, N., West, D., Al-Hadhrami, T., & Alazab, M. (2019). A Review of Automatic Phenotyping Approaches using Electronic Health Records. Electronics , 8(11), 1-23. [1235]. https://doi.org/10.3390/electronics8111235
Alzoubi, Hadeel ; Alzubi, Raid ; Ramzan, Naeem ; West, Daune ; Al-Hadhrami, Tawfik ; Alazab, Mamoun. / A Review of Automatic Phenotyping Approaches using Electronic Health Records. In: Electronics . 2019 ; Vol. 8, No. 11. pp. 1-23.
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abstract = "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.",
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Alzoubi, H, Alzubi, R, Ramzan, N, West, D, Al-Hadhrami, T & Alazab, M 2019, 'A Review of Automatic Phenotyping Approaches using Electronic Health Records', Electronics , vol. 8, no. 11, 1235, pp. 1-23. https://doi.org/10.3390/electronics8111235

A Review of Automatic Phenotyping Approaches using Electronic Health Records. / Alzoubi, Hadeel; Alzubi, Raid; Ramzan, Naeem; West, Daune; Al-Hadhrami, Tawfik; Alazab, Mamoun.

In: Electronics , Vol. 8, No. 11, 1235, 29.10.2019, p. 1-23.

Research output: Contribution to journalArticleResearchpeer-review

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AB - 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.

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