This paper is in the following e-collection/theme issue:

Natural Language Processing (650) Electronic Health Records (971)

Published on in Vol 7, No 4 (2019): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14782, first published .
Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach

Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach

Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach

Authors of this article:

Honghan Wu1, 2, 3 Author Orcid Image ;   Karen Hodgson4 Author Orcid Image ;   Sue Dyson4 Author Orcid Image ;   Katherine I Morley4, 5, 6 Author Orcid Image ;   Zina M Ibrahim4, 7 Author Orcid Image ;   Ehtesham Iqbal4 Author Orcid Image ;   Robert Stewart4, 5 Author Orcid Image ;   Richard JB Dobson4, 7 Author Orcid Image ;   Cathie Sudlow1, 3 Author Orcid Image
Article Authors Cited by (8) Tweetations (26) Metrics

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  3. Botelle R, Bhavsar V, Kadra-Scalzo G, Mascio A, Williams M, Roberts A, Velupillai S, Stewart R. Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study. BMJ Open 2022;12(2):e052911 View
  4. Wu H, Wang M, Wu J, Francis F, Chang Y, Shavick A, Dong H, Poon M, Fitzpatrick N, Levine A, Slater L, Handy A, Karwath A, Gkoutos G, Chelala C, Shah A, Stewart R, Collier N, Alex B, Whiteley W, Sudlow C, Roberts A, Dobson R. A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. npj Digital Medicine 2022;5(1) View
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