Most U.S. adults (68%) take dietary supplements and there is increasing evidence of drug-supplement interactions (DSIs); our ability to readily identify interactions between dietary supplements with prescription medications is currently very limited. To optimize the safe use of dietary supplements, there remains a critical and unmet need for informatics methods to detect DSIs. Our rationale is that an innovative translational informatics framework to discover potential DSIs from the biomedical literature with subsequent screening using clinical evidence from Electronic Health Records (EHR) will enable a new line of research for targeted DSI validation and will also significantly narrow the range of DSIs that must be further explored. Our long-term goal is to use informatics approaches to enhance DSI clinical research and translate its findings to clinical practice ultimately via clinical decision support systems. The objective of this application is to develop a translational informatics framework to enable the discovery of DSIs by linking scientific evidence from the biomedical literature and clinical evidence from our EHR systems. Towards these objectives, we propose the following specific aims: (1) Compile a comprehensive terminology of dietary supplements using online resources and EHR data; (2) Discover potential DSIs from the biomedical literature; and (3) Evaluate potential DSIs using clinical evidence obtained from EHR data. The successful accomplishment of this project will deliver a novel informatics paradigm and resources for identifying most clinically significant DSI signals and their biological mechanisms. This information is critical to subsequent efforts aimed at improving patient safety and efficacy of therapeutic interventions. The results from this study are imperative in order to achieve the ultimate goal of reducing an individual's risk of potential DSIs.
This research will address a critical and unmet need to conduct large-scale clinical research in drug- supplement interactions (DSIs) and improve evidence bases for healthcare practice. Our primary overarching goal is to use informatics approaches to enhance DSI clinical research and translate our findings to clinical practice ultimately via clinical decision support. The successful accomplishment of this project will deliver a novel informatics paradigm and valuable resources for identifying novel clinically significant DSI signals and their associated scientific evidence.
|Rizvi, Rubina F; Adam, Terrence J; Lindemann, Elizabeth A et al. (2018) Comparing Existing Resources to Represent Dietary Supplements. AMIA Jt Summits Transl Sci Proc 2017:207-216|
|Zhang, Rui; Meng, Jingjing; Lian, Qinshu et al. (2018) Prescription opioids are associated with higher mortality in patients diagnosed with sepsis: A retrospective cohort study using electronic health records. PLoS One 13:e0190362|
|Fan, Yadan; He, Lu; Pakhomov, Serguei V S et al. (2017) Classifying Supplement Use Status in Clinical Notes. AMIA Jt Summits Transl Sci Proc 2017:493-501|
|Zhang, Rui; Simon, Gyorgy; Yu, Fang (2017) Advancing Alzheimer's research: A review of big data promises. Int J Med Inform 106:48-56|
|Sun, Deyu; Simon, Gyorgy J; Skube, Steven et al. (2017) Causal Phenotyping for Susceptibility to Cardiotoxicity from Antineoplastic Breast Cancer Medications. AMIA Annu Symp Proc 2017:1655-1664|
|Sun, Deyu; Sarda, Gopal; Skube, Steven J et al. (2017) Phenotyping and Visualizing Infusion-Related Reactions for Breast Cancer Patients. Stud Health Technol Inform 245:599-603|
|Fan, Yadan; Adam, Terrence J; McEwan, Reed et al. (2017) Detecting Signals of Interactions Between Warfarin and Dietary Supplements in Electronic Health Records. Stud Health Technol Inform 245:370-374|
|Zhang, Rui; Pakhomov, Serguei V S; Arsoniadis, Elliot G et al. (2017) Detecting clinically relevant new information in clinical notes across specialties and settings. BMC Med Inform Decis Mak 17:68|
|Jian, Zhe; Guo, Xusheng; Liu, Shijian et al. (2017) A cascaded approach for Chinese clinical text de-identification with less annotation effort. J Biomed Inform 73:76-83|
|Fan, Yadan; He, Lu; Zhang, Rui (2017) Evaluating Automatic Methods to Extract Patients' Supplement Use from Clinical Reports. Proceedings (IEEE Int Conf Bioinformatics Biomed) 2017:1258-1261|
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