Classifying Eligibility Criteria in Clinical Trials Using Active Deep Learning

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

Abstract

In this paper we propose an active deep learning approach to automatically classify eligibility criteria of clinical trials, an application that has not been explored in machine learning. We collected all clinical trial data from the National Cancer Institute website, and applied word2vec to learn word embeddings for eligibility criteria. Criteria encoded with word embeddings were then fed into a multi-layer convolution neural network (CNN) for classification. To overcome the challenge of non-existing class labels, we designed an active learning algorithm that uses uncertainty cluster sampling to navigate the dataset and strategically propagate obtained labels to expand the training set for CNN. Experimental results show that word2vec successfully learns meaningful embeddings in criteria data, and the active deep learning approach reports a significant lower error rate in classification than the baseline k-nearest neighbor method.

Original languageEnglish (US)
Title of host publicationProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
EditorsM. Arif Wani, Moamar Sayed-Mouchaweh, Edwin Lughofer, Joao Gama, Mehmed Kantardzic
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages305-310
Number of pages6
ISBN (Electronic)9781538668047
DOIs
StatePublished - Jan 15 2019
Event17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States
Duration: Dec 17 2018Dec 20 2018

Publication series

NameProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018

Conference

Conference17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
CountryUnited States
CityOrlando
Period12/17/1812/20/18

Keywords

  • Active learning
  • Clinical trials
  • Convolution neural networks
  • Eligibility criteria
  • Word2vec

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Safety, Risk, Reliability and Quality
  • Signal Processing
  • Decision Sciences (miscellaneous)

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  • Cite this

    Chuan, C-H. (2019). Classifying Eligibility Criteria in Clinical Trials Using Active Deep Learning. In M. A. Wani, M. Sayed-Mouchaweh, E. Lughofer, J. Gama, & M. Kantardzic (Eds.), Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 (pp. 305-310). [8614077] (Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2018.00052