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High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP)

Abstract

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1–2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no).

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Fig. 1: PheCAP overview.
Fig. 2: Creating an NLP dictionary.
Fig. 3: Unsupervised feature learning.
Fig. 4: Detailed flow of PheCAP protocol.
Fig. 5: MetaMap output.
Fig. 6: NILE output.
Fig. 7: Algorithm-training step output.

Data availability

The datasets generated or analyzed in this protocol can be downloaded from https://celehs.github.io/PheCAP/.

Code availability

The R package and code referenced in this protocol can be downloaded from https://celehs.github.io/PheCAP/.

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Acknowledgements

We thank R. Eastwood for her assistance with figure design and Z. He for establishing the PheCAP website. We gratefully acknowledge support for this project from the NIH (P30 AR 072577; Tianrun C., C.H., J.H., Tianxi C., and K.P.L.) and a VA Office of Research and Development VA Merit Award (I01-CX001025; K.C., Y.L.H., J.H., D.G., C.O., J.M.G.); past support for i2b2 from the NIH (U54 LM008748; A.N.A., Z.X., S.Y.S., V.G., V.C., E.W.K., R.M.P., P.S., G.S., S.C., S.N.M., I.K., Tianxi C., and K.P.L.) and support from grant R01 HG009174 (V.G., V.C., and S.N.M.). A.N.A. received support from the Crohn’s and Colitis Foundation, the NIH, and Pfizer. Z.X. received support from the NIH (NINDS098023). S.H. received support from grant T32 AR 007530. K.P.L. received support from the Harold and DuVal Bowen Fund.

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Contributions

Y.Z., Tianrun C., S.Y., C.H., J.S., A.N.A., Z.X., S.Y.S., V.G., V.C., N.L., E.W.K., R.M.P., P.S., G.S., S.C., S.N.M., I.K., Tianxi C., and K.P.L. contributed to the development of pipeline; Y.Z., Tianrun C., S.Y., C.H., J.S., N.L., and Tianxi C. contributed to the development of the R package and software development used in this protocol; Y.Z., Tianrun C., K.C., C.H., J.S., J. Huang, Y.-L.H., A.N.A., Z.X., S.Y.S., V.G., V.C., N.L., J. Honerlaw, S.H., D.G., P.S., G.S,. S.C., C.O., S.N.M., J.M.G., I.K., Tianxi C., and K.P.L. contributed to the validation of and enhancements to the pipeline; Y.Z., Tianrun C., S.Y., C.H., J.S., V.G., V.C., G.S., Tianxi C., and K.P.L. drafted the manuscript; all authors contributed to revisions and proofreading of the manuscript.

Corresponding author

Correspondence to Katherine P. Liao.

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Competing interests

R.M.P. is employed at Celgene; however, his contributions to the protocol were performed while at Brigham and Women’s Hospital. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Juan Banda and other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Xia, Z. et al. PLoS One 8, e78927 (2013): https://doi.org/10.1371/journal.pone.0078927

Liao, K. P. et al. Ann. Rheum. Dis. 73, 1170–1175 (2014): https://doi.org/10.1136/annrheumdis-2012-203202

Liao, K. P. et al. BMJ 350, h1885 (2015): https://doi.org/10.1136/bmj.h1885

Ananthakrishnan, A. N. et al. Inflamm. Bowel Dis. 22, 151–158 (2016): https://doi.org/10.1097/MIB.0000000000000580

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Zhang, Y., Cai, T., Yu, S. et al. High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP). Nat Protoc 14, 3426–3444 (2019). https://doi.org/10.1038/s41596-019-0227-6

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