Browsing by Subject "Pharmacological network model"

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    Improved Adverse Drug Event Prediction Through Information Component Guided Pharmacological Network Model (IC-PNM)
    (IEEE, 2021) Ji, Xiangmin; Wang, Lei; Hua, Liyan; Wang, Xueying; Zhang, Pengyue; Shendre, Aditi; Feng, Weixing; Li, Jin; Li, Lang; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Improving adverse drug event (ADE) prediction is highly critical in pharmacovigilance research. We propose a novel information component guided pharmacological network model (IC-PNM) to predict drug-ADE signals. This new method combines the pharmacological network model and information component, a Bayes statistics method. We use 33,947 drug-ADE pairs from the FDA Adverse Event Reporting System (FAERS) 2010 data as the training data, and the new 21,065 drug-ADE pairs from FAERS 2011-2015 as the validations samples. The IC-PNM data analysis suggests that both large and small sample size drug-ADE pairs are needed in training the predictive model for its prediction performance to reach an area under the receiver operating characteristic curve (\textAUROC)= 0.82(AUROC)=0.82. On the other hand, the IC-PNM prediction performance improved to \textAUROC= 0.91AUROC=0.91 if we removed the small sample size drug-ADE pairs from the prediction model during validation.