COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching

@article{Gao2020COMPOSECP,
  title={COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching},
  author={Junyi Gao and Cao Xiao and Lucas Glass and Jimeng Sun},
  journal={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2020}
}
  • Junyi Gao, Cao Xiao, +1 author Jimeng Sun
  • Published 2020
  • Computer Science
  • Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Clinical trials play important roles in drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The availability of massive electronic health records (EHR) data and trial eligibility criteria (EC) bring a new opportunity to data driven patient recruitment. One key task named patient-trial matching is to find qualified patients for clinical trials given structured EHR and unstructured EC text (both inclusion and exclusion criteria). How to match complex… Expand
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