Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning

BACKGROUND: Electronic health records are invaluable for medical research, but much of the information is recorded as unstructured free text which is time-consuming to review manually. AIM: To develop an algorithm to identify relevant free texts automatically based on labelled examples. METHODS: We...

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Detalles Bibliográficos
Autores principales: Wang, Zhuoran, Shah, Anoop D., Tate, A. Rosemary, Denaxas, Spiros, Shawe-Taylor, John, Hemingway, Harry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261909/
https://www.ncbi.nlm.nih.gov/pubmed/22276193
http://dx.doi.org/10.1371/journal.pone.0030412

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261909/
https://www.ncbi.nlm.nih.gov/pubmed/22276193
http://dx.doi.org/10.1371/journal.pone.0030412

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