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Conference proceeding
Automated Off-label Drug Use Detection from User Generated Content
ACM-BCB' 2017: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics, pp 449-454
01 Jan 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Off-label drug use refers to using marketed drugs for indications that are not listed in their FDA labeling information. Such uses are very common and sometimes inevitable in clinical practice. To some extent, off-label drug uses provide a pathway for clinical innovation, however, they could cause serious adverse effects due to lacking scientific research and tests. Since identifying the off-label uses can provide a clue to the stakeholders including healthcare providers, patients, and medication manufacturers to further the investigation on drug efficacy and safety, it raises the demand for a systematic way to detect off-label uses. Given data contributed by health consumers in online health communities (OHCs), we developed an automated approach to detect off-label drug uses based on heterogeneous network mining. We constructed a heterogeneous healthcare network with medical entities (e.g. disease, drug, adverse drug reaction) mined from the text corpus, which involved 50 diseases, 1,297 drugs, and 185 ADRs, and determined 13 meta paths between the drugs and diseases. We developed three metrics to represent the meta-path-based topological features. With the network features, we trained the binary classifiers built on Random Forest algorithm to recognize the known drug-disease associations. The best classification model that used lift to measure path weights obtained F1-score of 0.87, based on which, we identified 1,009 candidates of off-label drug uses and examined their potential by searching evidence from PubMed and FAERS.
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Details
- Title
- Automated Off-label Drug Use Detection from User Generated Content
- Creators
- Mengnan Zhao - Drexel UniversityChristopher C. Yang - Drexel University
- Publication Details
- ACM-BCB' 2017: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics, pp 449-454
- Conference
- ACM-BCB '17: ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics, 8th (Boston, Massachusetts, United States, 20 Aug 2017–23 Aug 2017)
- Publisher
- ACM
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000426494700055
- Scopus ID
- 2-s2.0-85031321083
- Other Identifier
- 991019168295104721
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
- Mathematical & Computational Biology
- Medical Informatics