Accuracy of an automated knowledge base for identifying drug adverse reactions.
about
Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data.Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data.Association Between Serotonin Syndrome and Second-Generation Antipsychotics via Pharmacological Target-Adverse Event Analysis.OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies.Improving reproducibility by using high-throughput observational studies with empirical calibration
P2860
Accuracy of an automated knowledge base for identifying drug adverse reactions.
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
Accuracy of an automated knowledge base for identifying drug adverse reactions.
@en
Accuracy of an automated knowledge base for identifying drug adverse reactions.
@nl
type
label
Accuracy of an automated knowledge base for identifying drug adverse reactions.
@en
Accuracy of an automated knowledge base for identifying drug adverse reactions.
@nl
prefLabel
Accuracy of an automated knowledge base for identifying drug adverse reactions.
@en
Accuracy of an automated knowledge base for identifying drug adverse reactions.
@nl
P2093
P2860
P1476
Accuracy of an automated knowledge base for identifying drug adverse reactions
@en
P2093
J van der Lei
P R Rijnbeek
P2860
P356
10.1016/J.JBI.2016.12.005
P577
2016-12-16T00:00:00Z