Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records

TL;DR

This systematic review evaluates current methods for identifying patient phenotypes from electronic health records, highlighting the potential of natural language processing and the need for standardization and further research.

Contribution

It provides a comprehensive overview of phenotyping approaches, datasets, and emphasizes the importance of NLP and standardization in electronic health record analysis.

Findings

NLP is a promising approach for electronic phenotyping.

Challenges include data accessibility and lack of NLP standards.

Future research should focus on developing standards and evaluating machine learning methods.

Abstract

The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets. Special attention is given to preprocessing of in-put data and the different modeling approaches. The literature review confirms natural language processing to be a promising approach for electronic phenotyping. However, accessibility and lack of natural language process standards for medical texts remain a challenge. Future research should develop such standards and further investigate which machine learning approaches are best suited to which type of medical data.