Predicting High-Order Directional Drug-Drug Interaction Relations

Xia Ning, Li Shen, Lang Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

High-order Drug-Drug Interactions (DDI) are common particularly for elderly people. It is highly non-trivial to detect such interactions via in vivo/in vitro experiments. In this paper, we present SVM-based classification methods to predict whether a high-order directional drug-drug interaction (HoDDDI) instance is associated with adverse drug reactions (ADRs) and induced side effects. Specifically, we developed kernels for HoDDDI instances of arbitrary orders that are constructed from various single-drug information. The experiments over datasets extracted from electronic health records demonstrate that our classification methods can achieve the best F1 as 0.793.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
EditorsMollie Cummins, Julio Facelli, Gerrit Meixner, Christophe Giraud-Carrier, Hiroshi Nakajima
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages556-561
Number of pages6
ISBN (Electronic)9781509048816
DOIs
StatePublished - Sep 8 2017
Event5th IEEE International Conference on Healthcare Informatics, ICHI 2017 - Park City, United States
Duration: Aug 23 2017Aug 26 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017

Other

Other5th IEEE International Conference on Healthcare Informatics, ICHI 2017
CountryUnited States
CityPark City
Period8/23/178/26/17

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Keywords

  • High-Order Drug-Drug Interactions
  • Support Vector Machines

ASJC Scopus subject areas

  • Health Informatics

Cite this

Ning, X., Shen, L., & Li, L. (2017). Predicting High-Order Directional Drug-Drug Interaction Relations. In M. Cummins, J. Facelli, G. Meixner, C. Giraud-Carrier, & H. Nakajima (Eds.), Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017 (pp. 556-561). [8031212] (Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2017.76