@inproceedings{32504835d353431cab841304ddf7aa9c,
title = "Classifying Eligibility Criteria in Clinical Trials Using Active Deep Learning",
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.",
keywords = "Active learning, Clinical trials, Convolution neural networks, Eligibility criteria, Word2vec",
author = "Ching-Hua Chuan",
year = "2019",
month = jan,
day = "15",
doi = "10.1109/ICMLA.2018.00052",
language = "English (US)",
series = "Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "305--310",
editor = "Wani, {M. Arif} and Moamar Sayed-Mouchaweh and Edwin Lughofer and Joao Gama and Mehmed Kantardzic",
booktitle = "Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018",
note = "17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 ; Conference date: 17-12-2018 Through 20-12-2018",
}