Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network

Jennifer A. Pacheco, Luke V. Rasmussen, Ken Wiley, Thomas Nate Person, David J. Cronkite, Sunghwan Sohn, Shawn Murphy, Justin H. Gundelach, Vivian Gainer, Victor M. Castro, Cong Liu, Frank Mentch, Todd Lingren, Agnes S. Sundaresan, Garrett Eickelberg, Valerie Willis, Al’ona Furmanchuk, Roshan Patel, David S. Carrell, Yu DengNephi Walton, Benjamin A. Satterfield, Iftikhar J. Kullo, Ozan Dikilitas, Joshua C. Smith, Josh F. Peterson, Ning Shang, Krzysztof Kiryluk, Yizhao Ni, Yikuan Li, Girish N. Nadkarni, Elisabeth A. Rosenthal, Theresa L. Walunas, Marc S. Williams, Elizabeth W. Karlson, Jodell E. Linder, Yuan Luo, Chunhua Weng, Wei Qi Wei

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.

Original languageEnglish
Article number1971
JournalScientific Reports
Issue number1
StatePublished - Dec 2023


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