Most U.S. adults (68%) take dietary supplements and there is increasing evidence of drug-supplement interactions (DSIs); In recent years, there has been increasing evidence supporting the role of DSs in ADRD in preventing cognitive impairment but there is limited evidence and the sample sizes have been small. Real- world data (RWD) especially the EHR contain detailed treatment and response information from patients and could be used to detect the usage and effect of DSs, DSIs, which is more translational to clinical outcomes (e.g., MCI to ADRD conversion). To the best of our knowledge, there is no investigation on DSs usage and safety among patients in MCI and ADRD using EHR data. Our current parent award is focusing on the development of a translational informatics framework to enable the discovery of drug-supplement interactions (DSIs) by linking scientific evidence from the biomedical literature. To response to NOT-AG-20-008, this administrative supplement application will complement our parent award in multiple aspects: (1) developing novel and advanced data analytic methods for mining RWD in EHR, (2) identifying DSs usage information among patients with ADRD, and (3) detecting safety and effect of DS among patients with ADRD from existing EHR data. In our preliminary work, we have investigated the methods to identify DSs terms on EHR and developed natural language processing (NLP) methods to identify use status of DSs. We will further our efforts to collect a EHR dataset with DSs usage and AE-DSs signals from AD patients and develop innovative informatics methods to extract such information.
Our specific aims are: (1) identifying DSs usage among patients with MCI and ADRD from existing EHR data; and (2) detecting the DSs safety signals and exploring the effect of DSs use on the conversion from MCI to ADRD from existing EHR data. The successful completion of this project will stimulate our further investigation on the role of DS use in patients with ADRD in a larger scale involving EHR data from other healthcare institutions.
In this administrative supplement application, we will evaluate the usage and safety of dietary supplements (DSs) usage in patients with Mild Cognitive Impairment (MCI) and Alzheimer?s disease and related dementias (ADRD), respectively using electronic health records (EHR) data. We will also explore the feasibility of using EHR to detect DS effect on the conversion from MCI to ADRD. This research will address a critical and unmet need to conduct large-scale clinical research in DSs and improve evidence bases for healthcare practice. The successful accomplishment of this supplement project will deliver a novel informatics methods and generate DSI signals among patients with ADRD. This project will stimulate our further investigation on the role of DS use in patients with ADRD in a larger scale involving EHR data from other healthcare institutions.
| 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 |
| 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 |
| Wang, Yefeng; Gunashekar, Divya R; Adam, Terrence J et al. (2017) Mining Adverse Events of Dietary Supplements from Product Labels by Topic Modeling. Stud Health Technol Inform 245:614-618 |
| 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 |
Showing the most recent 10 out of 11 publications
