Development of phenotype algorithms using electronic medical records and incorporating natural language processing

By Katherine P. Liao, Tianxi Cai, Guergana K. Savova, Shawn N. Murphy, Elizabeth W. Karlson, Ashwin N. Ananthakrishnan, Vivian S. Gainer, Stanley Y. Shaw, Zongqi Xia, Peter Szolovits and 2 more. 2015

269 citations recorded by the source corpus.

Electronic medical records are emerging as a major source of data for clinical and translational research studies, although phenotypes of interest need to be accurately defined first. This article provides an overview of how to develop a phenotype algorithm from electronic medical records, incorporating modern informatics and biostatistics methods.

Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “geneous data sources in addition to structured data, such as clinical narratives, public databases, social media, biomedical literature [15], [88], [101], [111], [115], [175], [176].”
Electronic medical record phenotyping using the anchor and learn framework (2016, background): “19 Phenotypes in PheKB are developed manually through a rigorous process, requiring multiple iterations and eventual physician consensus.”
Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, methodology): “Natural language processing methods can extract this valuable data, which in conjunction with analyzing structured data can lead to a better understanding of health and diseases [13] and to a more accurate phenotyping of patients to compare tests and treatments [14–16].”
Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances (2018, unknown): “enable the improvement of case identification from health records [46,47] and can provide a much richer set of data than could be achieved by the use of structured data alone.”
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping (2017, methodology): “Liao et al.(12) describe the process of extraction, rule-generation and prediction as the general approach to patient phenotyping using the cTAKES, and test this approach on various data sets.”