TY - JOUR
T1 - A Topic-modeling Based Framework for Drug-drug Interaction Classification from Biomedical Text
AU - Li, Dingcheng
AU - Liu, Sijia
AU - Rastegar-Mojarad, Majid
AU - Wang, Yanshan
AU - Chaudhary, Vipin
AU - Therneau, Terry
AU - Liu, Hongfang
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
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M3 - Article
C2 - 28269875
AN - SCOPUS:85027488840
SN - 1559-4076
VL - 2016
SP - 789
EP - 798
JO - AMIA ... Annual Symposium proceedings. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings. AMIA Symposium
ER -