Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review-Reference-Cited by-同舟云学术

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Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review

Published:2017-06-22 Issue:11 Volume:40 Page:1075-1089
ISSN:0114-5916
Container-title:Drug Safety
language:en
Short-container-title:Drug Saf

Author:

Luo Yuan,Thompson William K.,Herr Timothy M.,Zeng Zexian,Berendsen Mark A.,Jonnalagadda Siddhartha R.,Carson Matthew B.,Starren Justin

Funder

AbbVie

Publisher

Springer Science and Business Media LLC

Subject

Pharmacology (medical),Pharmacology,Toxicology

Link

http://link.springer.com/article/10.1007/s40264-017-0558-6/fulltext.html

Reference113 articles.

1. Onder G, et al. Adverse drug reactions as cause of hospital admissions: results from the Italian Group of Pharmacoepidemiology in the Elderly (GIFA) (in English). J Am Geriatr Soc. 2002;50(12):1962–8.

2. Harpaz R, et al. Text mining for adverse drug events: the promise, challenges, and state of the art. Drug Saf. 2014;37(10):777–90.

3. Cami A, Arnold A, Manzi S, Reis B. Predicting adverse drug events using pharmacological network models (in English). Sci Transl Med. 2011;3(114):114ra127. doi: 10.1126/scitranslmed.3002774 .

4. Pouliot Y, Chiang AP, Butte AJ. Predicting adverse drug reactions using publicly available PubChem BioAssay data (in English). Clin Pharmacol Ther. 2011;90(1):90–9.

5. Zheng HR, Wang HY, Xu H, Wu YH, Zhao ZM, Azuaje F. Linking biochemical pathways and networks to adverse drug reactions (in English). IEEE Trans Nanobiosci. 2014;13(2):131–7.

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