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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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Exploiting OHC Data with Tensor Decomposition for Off-Label Drug Use Detection
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.
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- Award ID(s):
- 1650531
- PAR ID:
- 10139468
- Date Published:
- Journal Name:
- Proceedings of the IEEE International Conference on Healthcare Informatics 2018
- Page Range / eLocation ID:
- 22 to 28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Off-label drug use is quite common in clinical practice and inevitable to some extent. Such uses might deliver effective treatment and suggest clinical innovation sometimes, however, they have the unknown risk to cause serious outcomes due to lacking scientific support. As gaining information about off-label drug use could present a clue to the stakeholders such as healthcare professionals and medication manufacturers to further the investigation on drug efficacy and safety, it raises the need to develop a systematic way to detect off-label drug uses. Considering the increasing discussions in online health communities (OHCs) among the health consumers, we proposed to harness the large volume of timely information in OHCs to develop an automated method for detecting off-label drug uses from health consumer generated data. From the text corpus, we extracted medical entities (diseases, drugs, and adverse drug reactions) with lexicon-based approaches and measured their interactions with word embedding models, based on which, we constructed a heterogeneous healthcare network. We defined several meta-path-based indicators to describe the drug-disease associations in the heterogeneous network and used them as features to train a binary classifier built on Random Forest algorithm, to recognize the known drug-disease associations. The classification model obtained better results when incorporating word embedding features and achieved the best performance when using both association rule mining features and word embedding features, with F1-score reaching 0.939, based on which, we identified 2,125 possible off-label drug uses and checked their potential by searching evidence in PubMed and FAERS.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICHI.2018.00010
