Conference proceeding
Exploiting OHC Data with Tensor Decomposition for Off-Label Drug Use Detection
2018 IEEE International Conference on Healthcare Informatics (ICHI)
Jun 2018
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Off-label drug use is an important healthcare topic as it is quite common and sometimes inevitable in medical practice. Though gaining information about off-label drug uses could benefit a lot of healthcare stakeholders such as patients, physicians, and pharmaceutical companies, there is no such data repository of such information available. There is a desire for a systematic approach to detect off-label drug uses. Other than using data sources such as EHR and clinical notes that are provided by healthcare providers, we exploited social media data especially online health community (OHC) data to detect the off-label drug uses, with consideration of the increasing social media users and the large volume of valuable and timely user-generated contents. We adopted tensor decomposition technique, CP decomposition in this work, to deal with the sparsity and missing data problem in social media data. On the basis of tensor decomposition results, we used two approaches to identify off-label drug use candidates: (1) one is via ranking the CP decomposition resulting components, (2) the other one is applying a heterogeneous network mining method, proposed in our previous work [9], on the reconstructed dataset by CP decomposition. The first approach identified a number of significant off-label use candidates, for which we were able to conduct case studies and found medical explanations for 7 out of 12 identified off-label use candidates. The second approach achieved better performance than the previous method [9] by improving the F1-score by 3%. It demonstrated the effectiveness of performing tensor decomposition on social media data for detecting off-label drug use.
Metrics
Details
- Title
- Exploiting OHC Data with Tensor Decomposition for Off-Label Drug Use Detection
- Creators
- Mengnan Zhao - Drexel UniversityChristopher C Yang - Drexel University
- Publication Details
- 2018 IEEE International Conference on Healthcare Informatics (ICHI)
- Conference
- 2018 IEEE International Conference on Healthcare Informatics (ICHI)
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000853207500003
- Scopus ID
- 2-s2.0-85051113316
- Other Identifier
- 991019173720604721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Health Care Sciences & Services
- Medical Informatics