Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records
This review addresses the problem of efficiently identifying patient phenotypes for healthcare researchers and practitioners, but it is incremental as it synthesizes existing literature without new empirical results.
The paper conducted a systematic review of approaches to detect patient cohorts from electronic health records, identifying natural language processing as a promising method but highlighting challenges like accessibility and lack of standards in medical texts.
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.