A supervised adverse drug reaction signalling framework imitating Bradford Hill's causality considerations
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data.
Big dataPharmacovigilanceLongitudinal observational dataCausal effectsSignal detection
Aickelin, Uwe、Gibson, Jack E.、Hubbard, Richard B.、Reps, Jenna Marie、Garibaldi, Jonathan M.
展开 >
Univ Nottingham, Sch Comp Sci, Nottingham NG8 1BB, England
Univ Nottingham, Div Epidemiol & Publ Hlth, Nottingham NG8 1BB, England
NSTL
Elsevier
