Journal article
Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance
Artificial intelligence in medicine, v 90, pp 42-52
Aug 2018
PMID: 30093253
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Drug safety, also called pharmacovigilance, represents a serious health problem all over the world. Adverse drug reactions (ADRs) and drug-drug interactions (DDIs) are two important issues in pharmacovigilance, and how to detect drug safety signals has drawn many researchers' attention and efforts. Currently, methods proposed for ADR and DDI detection are mainly based on traditional data sources such as spontaneous reporting data, electronic health records, pharmaceutical databases, and biomedical literature. However, these data sources are either limited by under-reporting ratio, privacy issues, high cost, or long publication cycle. In this study, we propose a framework for drug safety signal detection by harnessing online health community data, a timely, informative, and publicly available data source. Concretely, we used MedHelp as the data source to collect patient-contributed content based on which a weighted heterogeneous network was constructed. We extracted topological features from the network, quantified them with different weighting methods, and used supervised learning method for both ADR and DDI signal detection. In addition, after identifying DDI signals, we proposed a new metric, named Interaction Ratio, to identify associated ADRs due to suspected interactions. The experiment results showed that our proposed techniques outperforms baseline methods.
Metrics
Details
- Title
- Mining heterogeneous networks with topological features constructed from patient-contributed content for pharmacovigilance
- Creators
- Christopher C Yang - College of Computing and Informatics, Drexel University, United StatesHaodong Yang - College of Computing and Informatics, Drexel University, United States
- Publication Details
- Artificial intelligence in medicine, v 90, pp 42-52
- Publisher
- Elsevier; Netherlands
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000460857100005
- Scopus ID
- 2-s2.0-85050995931
- Other Identifier
- 991014877664504721
UN Sustainable Development Goals (SDGs)
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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
- Engineering, Biomedical
- Medical Informatics