Browsing by Subject "Adverse drug event (ADE)"

Now showing 1 - 2 of 2
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    A theoretical model for detecting drug interaction with awareness of timing of exposure
    (Springer Nature, 2025-04-21) Shi, Yi; Sun, Anna; Yang, Yuedi; Xu, Jing; Li, Justin; Eadon, Michael; Su, Jing; Zhang, Pengyue; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Drug-drug interaction-induced (DDI-induced) adverse drug event (ADE) is a significant public health burden. Risk of ADE can be related to timing of exposure (TOE) such as initiating two drugs concurrently or adding one drug to an existing drug. Thus, real-world data based DDI detection shall be expanded to investigate precise adverse DDI with a special awareness on TOE. We developed a Sensitive and Timing-awarE Model (STEM), which was able to optimize the probability of detection and control false positive rate for mining all two-drug combinations under case-crossover design, in particular for DDIs with TOE-dependent risk. We analyzed a large-scale US administrative claims data and conducted performance evaluation analyses. We identified signals of DDIs by using STEM, in particular for DDIs with TOE-dependent risk. We also observed that STEM identified significantly more signals than the conditional logistic regression model-based (CLRM-based) methods and the Benjamini-Hochberg procedure. In the performance evaluation, we found that STEM demonstrated proper false positive control and achieved a higher probability of detection compared to CLRM-based methods and the Benjamini-Hochberg procedure. STEM has a high probability to identify signals of DDIs in high-throughput DDI mining while controlling false positive rate, in particular for detecting signals of DDI with TOE-dependent risk.
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    Using multiple drug similarity networks to promote adverse drug event detection
    (Elsevier, 2024-11-05) Padhi, Biswajit; Liu, Ruoqi; Yang, Yuedi; Peng, Xueqiao; Li, Lang; Zhang, Pengyue; Zhang, Ping; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://drug-drug-sim.aimedlab.net/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring.