Clinical trials, which test the safety and efficacy of drugs in a controlled population, cannot identify all safety issues associated with drugs because the size and characteristics of the target population, duration of use, the concomitant disease conditions and therapies differ markedly in actual usage conditions. On the outpatient side, medication related morbidity and mortality in the United States is estimated to result in 100,000 deaths and $177 billion in cost annually. On the inpatient side, it is estimated that roughly 30% of hospital stays have an adverse drug event. Current one-drug-at-a-time methods for surveillance are woefully inadequate because no one monitors the """"""""real life"""""""" situation of patients getting over 3 concomitant drugs in the context of multiple co-morbidities. In preliminary work, we have built an annotation and analysis pipeline that uses the knowledge-graph formed by public biomedical ontologies for the purpose data-mining unstructured clinical notes. We have demonstrated that we can reproduce drug safety signals from the clinical notes on average 2.7 years ahead of the issue of a drug safety alert. Using this pipeline, we propose: 1) to identfy and prioritize multi-drug combinations that are worth testing;2) to develop methods for discovering adverse event profiles of multi- drug combinations;and 3) to create an EHR derived catalogue of potential adverse events of multi-drug combinations. We will use hierarchies provided by existing public ontologies for drugs, diseases and side- effects to improve signal detection by aggregation, to reduce multiple hypothesis testing and to make a search for multi-drug side effects computationally tractable.
We propose to combine data from electronic medical records, adverse event reports in AERS, and prior knowledge in curated knowledgebases to construct a data-driven safety profile for drugs. Successful development of novel methods will result in significant cost savings as well as a significant increase in patient safety, given the current rat (~30%) of occurrence of adverse drug events. Completion of the aims will result in the first of it kind, EHR derived, resource of adverse event profiles of drugs.
|Coulet, Adrien; Shah, Nigam H; Wack, Maxime et al. (2018) Predicting the need for a reduced drug dose, at first prescription. Sci Rep 8:15558|
|Kwong, Calvin; Ling, Albee Y; Crawford, Michael H et al. (2017) A Clinical Score for Predicting Atrial Fibrillation in Patients with Cryptogenic Stroke or Transient Ischemic Attack. Cardiology 138:133-140|
|Banda, Juan M; Evans, Lee; Vanguri, Rami S et al. (2016) A curated and standardized adverse drug event resource to accelerate drug safety research. Sci Data 3:160026|
|Oellrich, Anika; Collier, Nigel; Groza, Tudor et al. (2016) The digital revolution in phenotyping. Brief Bioinform 17:819-30|
|Agarwal, Vibhu; Podchiyska, Tanya; Banda, Juan M et al. (2016) Learning statistical models of phenotypes using noisy labeled training data. J Am Med Inform Assoc 23:1166-1173|
|Agarwal, Vibhu; Zhang, Liangliang; Zhu, Josh et al. (2016) Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis. J Med Internet Res 18:e251|
|Chen, Jonathan H; Humphreys, Keith; Shah, Nigam H et al. (2016) Distribution of Opioids by Different Types of Medicare Prescribers. JAMA Intern Med 176:259-61|
|Hripcsak, George; Ryan, Patrick B; Duke, Jon D et al. (2016) Characterizing treatment pathways at scale using the OHDSI network. Proc Natl Acad Sci U S A 113:7329-36|
|Low, Yen Sia; Gallego, Blanca; Shah, Nigam Haresh (2016) Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records. J Comp Eff Res 5:179-92|
|Agarwal, Vibhu; Han, Lichy; Madan, Isaac et al. (2016) Predicting hospital visits from geo-tagged Internet search logs. AMIA Jt Summits Transl Sci Proc 2016:15-24|
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