A review of approaches to identifying patient phenotype cohorts using electronic health records

By Chaitanya P. Shivade, Preethi Raghavan, Eric Fosler-Lussier, P. Embí, Noémie Elhadad, Stephen B. Johnson, Albert Max Lai. 2013

528 citations recorded by the source corpus.

Objective To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. Materials and methods We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included. Results Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. Discussion We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. Conclusions There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

Subfields: Artificial Intelligence, Genetics, Health Information Management

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Further, rules-based algorithms have been popular in EHR-based research, but because these are developed at a single institution and trained with a specific patient population, they do not transfer easily to other healthcare systems [107].”
Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “In addition, rule-based systems are one of the most prevalent methods for NLP-based computational phenotyping [3].”
Task-oriented Dialogue System for Automatic Diagnosis (2018, background): “Automatic phenotype identiﬁcation using electronic health records (EHRs) has been a rising topic in recent years (Shivade et al., 2013).”
Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, background): “While the term EHR-based phenotyping has various meanings [28], this paper focuses primarily on the process of extracting medical concepts, or phenotypes.”
Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis (2015, background): “In other cases, NLP is part of a grander scheme dealing with problems that require competence from several areas, e.g. when connecting genes to reported patient phenotypes extracted from EHRs [82-83].”
Electronic medical record phenotyping using the anchor and learn framework (2016, background): “16 While there has been considerable success in sharing community-built phenotypes for research purposes (eg, the PheKB knowledge base), 17 there has been less work on building phenotypes for activating clinical decision support in real time.”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, background): “The work by Shivade et al [14] reviews 97 papers aiming at identifying patient cohorts for further medical studies.”
A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases (2015, methodology): “To address the five document-level binary disease classification tasks, we utilize SVMs.SVM classifiers have seen wide use in the phenotyping literature (Marafino et al., 2014; Shivade et al., 2014; Wright et al., 2013).”
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability (2016, background): “In the setting of performing research on a given condition, PPV has generally been considered a more important metric than sensitivity, 22,44,45 assuming sufficient sample size and that exclusions of cases are typically based on lack of available health care data instead of asserting a systematic…”
A machine learning-based framework to identify type 2 diabetes through electronic health records (2016, background): “However, these controls were excluded in expert algorithms [11,13,15,30].”
Mining Electronic Health Records: A Survey (2017, background): “For a detailed review of phenotyping, the reader is referred to Shivade et al. [Shivade et al. 2014].”