Identifying risk of opioid use disorder for patients taking opioid medications with deep learning
Author Fusheng Wang, PhD, discusses this month's JAMIA Journal Club selection:
Dong X, Deng J, Rashidian S, et al. Identifying risk of opioid use disorder for patients taking opioid medications with deep learning. J Am Med Inform Assoc. 2021;28(8):1683-1693. doi:10.1093/jamia/ocab043
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Author
Fusheng Wang, PhD
Dr. Fusheng Wang is an Associate Professor in the Department of Biomedical Informatics and Department of Computer Science at Stony Brook University. He received his Ph.D. in Computer Science from University of California, Los Angeles, and M.S. and B.S. in Engineering Physics from Tsinghua University. Prior to joining Stony Brook University, he was an Assistant Professor at Emory University. He was a research scientist at Siemens Corporate Research (Princeton, NJ) before joining Emory University. His research interests crosscut biomedical informatics and computer science, including big data management and analytics, GIS, AI in Healthcare, medical imaging informatics, population health and opioid epidemic research. He has developed multiple big spatial data management systems for effectively managing, querying and mining of multiple dimensional data at extreme scale, including 2D and 3D data. He has applied such methods for multiple biomedical applications, including public health and computational digital pathology. He developed various machine learning/deep learning based models on opioid overdose and opioid use disorder risk prediction, understanding drug-drug interaction, and discovering novel opioid antagonists. He has published more than 160 peer-reviewed manuscripts in biomedical informatics and computer science.
Moderator
Ziyou Ren, PhD
Manager
Hannah Burkhardt
Statement of Purpose
The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions.
Target Audience
The target audience for this activity is professionals and students interested in health informatics.
Learning Objectives
After participating in this webinar the listener should be better able to:
- Describe the need of risk prediction of opioid use disorder for early interventions for combating opioid epidemic.
- Explain why temporal deep learning based methods offer promise for the development of predictive models for opioid use disorder.
- Describe the pipeline for building the predictive models from large scale electronic health records.
- Identify important features that help to understand the predictions for potential clinical decision support.
Format
- 35-minute presentation by article author(s) considering salient features of the published study and its potential impact on practice.
- 25-minute discussion of questions submitted by listeners via the webinar tools and moderated by JAMIA Student Editorial Board members.
Accreditation Statement
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
Credit Designation Statement
The American Medical Informatics Association designates this live activity for a maximum of 1 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
The live webinar only offers CME credit. The recording on our website will be openly available for learners but will not offer CME credit.
Commercial Support
No commercial support was received for this activity.
Disclosures for this Activity
The following planners, and staff who are in a position to control the content of this activity disclose that they have no relevant financial relationships with commercial interests/ineligible entities:
- Presenter: Fusheng Wang
- JAMIA Journal Club planners: Hannah Burkhardt, Kirk E. Roberts, Ziyou Ren
- AMIA staff: Susanne Arnold, Pesha Rubinstein
Instructions for Claiming CME Credit
Use the link in the webinar’s chat area to access the claim-credit survey; in a day or two you will receive an email with your CME certificate.
If you require a certificate of participation, contact Pesha@amia.org.