title: A Topic-modeling Based Framework for Drug-drug Interaction Classification from Biomedical Text.
abstract: Classification of drug-drug interaction (DDI) from medical literatures is significant in preventing medication-related errors. Most of the existing machine learning approaches are based on supervised learning methods. However, the dynamic nature of drug knowledge, combined with the enormity and rapidly growing of the biomedical literatures make supervised DDI classification methods easily overfit the corpora and may not meet the needs of real-world applications. In this paper, we proposed a relation classification framework based on topic modeling (RelTM) augmented with distant supervision for the task of DDI from biomedical text. The uniqueness of RelTM lies in its two-level sampling from both DDI and drug entities. Through this design, RelTM take both relation features and drug mention features into considerations. An efficient inference algorithm for the model using Gibbs sampling is also proposed. Compared to the previous supervised models, our approach does not require human efforts such as annotation and labeling, which is its advantage in trending big data applications. Meanwhile, the distant supervision combination allows RelTM to incorporate rich existing knowledge resources provided by domain experts. The experimental results on the 2013 DDI challenge corpus reach 48% in F1 score, showing the effectiveness of RelTM.
authors.affiliation: These authors contributed equally to this study and Dr. Li is now working at IBM; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Dingcheng
authors.initials: D
authors.lastname: Li
authors.affiliation: These authors contributed equally to this study and Dr. Li is now working at IBM; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
authors.forename: Sijia
authors.initials: S
authors.lastname: Liu
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Majid
authors.initials: M
authors.lastname: Rastegar-Mojarad
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Yanshan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
authors.forename: Vipin
authors.initials: V
authors.lastname: Chaudhary
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Terry
authors.initials: T
authors.lastname: Therneau
authors.affiliation: These authors contributed equally to this study and Dr. Li is now working at IBM.
authors.forename: Hongfang
authors.initials: H
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D004347:Drug Interactions; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D000069550:Machine Learning