Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference

Authors

  • Yichao Zhou University of California, Los Angeles
  • Yu Yan University of California, Los Angeles
  • Rujun Han University of Southern California
  • J. Harry Caufield University of California, Los Angeles
  • Kai-Wei Chang University of California, Los Angeles
  • Yizhou Sun University of California, Los Angeles
  • Peipei Ping University of California, Los Angeles
  • Wei Wang University of California, Los Angeles

Keywords:

Information Extraction, Applications, Healthcare, Medicine & Wellness

Abstract

There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive feature engineering or are incapable of modeling the global relational dependencies among the events. In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level. Extensive experiments on two benchmark datasets, I2B2-2012 and TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods for temporal relation extraction.

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Published

2021-05-18

How to Cite

Zhou, Y., Yan, Y., Han, R., Caufield, J. H., Chang, K.-W., Sun, Y., Ping, P., & Wang, W. (2021). Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14647-14655. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17721

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing III