Author

Courtney Rees Lyles

San Francisco General HospitalORCID 0000-0002-1111-8595Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Assay:electronic health records· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Andrew John KarteriD· 1 shared Danielle S. McNamaraiD· 1 shared Dean SchillingeriD· 1 shared Jennifer Y. LiuiD· 1 shared Renu BalyaniD· 1 shared Scott A. CrossleyiD· 1 shared William BrowniD· 1 shared

Activity by Year

2019-2019 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Andrew John KarterORCID

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1 paper

Recent·Most cited

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study

Renu Balyan, Scott A. Crossley, William Brown, Andrew John Karter, Danielle S. McNamara, Jennifer Y. Liu, Courtney Rees Lyles, Dean Schillinger

This study uses natural language processing and machine learning to classify health literacy from secure patient messages, identifying patterns associated with limited health literacy and poorer health outcomes.

PLoS ONE · Feb 22, 2019Peer-reviewedBioinformatics