Cargando…
Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records
Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph co...
Autores principales: | Bean, Daniel M., Wu, Honghan, Iqbal, Ehtesham, Dzahini, Olubanke, Ibrahim, Zina M., Broadbent, Matthew, Stewart, Robert, Dobson, Richard J. B. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703951/ https://www.ncbi.nlm.nih.gov/pubmed/29180758 http://dx.doi.org/10.1038/s41598-017-16674-x |
Ejemplares similares
-
Author Correction: Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records
por: Bean, Daniel M., et al.
Publicado: (2018) -
ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records
por: Iqbal, Ehtesham, et al.
Publicado: (2017) -
Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register
por: Iqbal, Ehtesham, et al.
Publicado: (2015) -
Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach
por: Wu, Honghan, et al.
Publicado: (2019) -
The side effect profile of Clozapine in real world data of three large mental health hospitals
por: Iqbal, Ehtesham, et al.
Publicado: (2020)