Mining FDA drug labels using an unsupervised learning technique - topic modeling

BMC Bioinformatics

Proceedings

Zhichao Liu¹  Weida Tong¹  Halil Bisgin²  Xiaowei Xu³  Hong Fang⁴ 

[1] Center for Bioinformatics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, 72079, Jefferson, AR, USA;Department of Information Science, University of Arkansas at Little Rock, 2801 S. University Ave., 72204-1099, Little Rock, AR, USA;Department of Information Science, University of Arkansas at Little Rock, 2801 S. University Ave., 72204-1099, Little Rock, AR, USA;Center for Bioinformatics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, 72079, Jefferson, AR, USA;ICF International at FDA's National Center for Toxicological Research, 3900 NCTR Rd, 72079, Jefferson, AR, USA;

关键词: Topic Modeling;    Latent Dirichlet Allocation;    Boxed Warning;    Latent Semantic Indexing;    Drug Label;   

DOI  :  10.1186/1471-2105-12-S10-S11

来源: Springer

【 摘 要 】

BackgroundThe Food and Drug Administration (FDA) approved drug labels contain a broad array of information, ranging from adverse drug reactions (ADRs) to drug efficacy, risk-benefit consideration, and more. However, the labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the labeling text in a consistent and accurate fashion for comparative analysis across drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive.MethodIn this study, a novel text mining method with unsupervised learning in nature, called topic modeling, was applied to the drug labeling with a goal of discovering “topics” that group drugs with similar safety concerns and/or therapeutic uses together. A total of 794 FDA-approved drug labels were used in this study. First, the three labeling sections (i.e., Boxed Warning, Warnings and Precautions, Adverse Reactions) of each drug label were processed by the Medical Dictionary for Regulatory Activities (MedDRA) to convert the free text of each label to the standard ADR terms. Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of drugs grouped together based on the probability analysis. Lastly, the efficacy of the topic modeling was evaluated based on known information about the therapeutic uses and safety data of drugs.ResultsThe results demonstrate that drugs grouped by topics are associated with the same safety concerns and/or therapeutic uses with statistical significance (P<0.05). The identified topics have distinct context that can be directly linked to specific adverse events (e.g., liver injury or kidney injury) or therapeutic application (e.g., antiinfectives for systemic use). We were also able to identify potential adverse events that might arise from specific medications via topics.ConclusionsThe successful application of topic modeling on the FDA drug labeling demonstrates its potential utility as a hypothesis generation means to infer hidden relationships of concepts such as, in this study, drug safety and therapeutic use in the study of biomedical documents.

【 授权许可】

Unknown   
© Bisgin et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.