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Automated Off-label Drug Use Detection from User Generated Content

Authors:

Christopher C. YangAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 449 - 454

https://doi.org/10.1145/3107411.3107475

Published: 20 August 2017 Publication History

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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Digital Library

Cited By

Gao YXu AHu P(2024)Mining Patient-Generated Content for Medication Relations and Transition Network to Predict the Rankings and Volumes of Different MedicationsInformation Systems Frontiers10.1007/s10796-024-10530-wOnline publication date: 7-Sep-2024
https://doi.org/10.1007/s10796-024-10530-w
Audeh BBellet FBeyens MLillo-Le Louët ABousquet C(2020)Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med ProjectDrug Safety10.1007/s40264-020-00951-2Online publication date: 16-Jun-2020
https://doi.org/10.1007/s40264-020-00951-2
Zhao MYang C(2018)Exploiting OHC Data with Tensor Decomposition for Off-Label Drug Use Detection2018 IEEE International Conference on Healthcare Informatics (ICHI)10.1109/ICHI.2018.00010(22-28)Online publication date: Jun-2018
https://doi.org/10.1109/ICHI.2018.00010

Index Terms

Automated Off-label Drug Use Detection from User Generated Content
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

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Published In

cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 20 August 2017

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BCB '17

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BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

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ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Gao YXu AHu P(2024)Mining Patient-Generated Content for Medication Relations and Transition Network to Predict the Rankings and Volumes of Different MedicationsInformation Systems Frontiers10.1007/s10796-024-10530-wOnline publication date: 7-Sep-2024
https://doi.org/10.1007/s10796-024-10530-w
Audeh BBellet FBeyens MLillo-Le Louët ABousquet C(2020)Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med ProjectDrug Safety10.1007/s40264-020-00951-2Online publication date: 16-Jun-2020
https://doi.org/10.1007/s40264-020-00951-2
Zhao MYang C(2018)Exploiting OHC Data with Tensor Decomposition for Off-Label Drug Use Detection2018 IEEE International Conference on Healthcare Informatics (ICHI)10.1109/ICHI.2018.00010(22-28)Online publication date: Jun-2018
https://doi.org/10.1109/ICHI.2018.00010

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