Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
This work is significant for the medical community, providing more precise temporal information that can facilitate downstream applications like case report retrieval and medical question answering.
This paper addresses the extraction of temporal relations between clinical events at the document level. The proposed method, CTRL-PG, significantly outperforms baseline methods on the I2B2-2012 and TB-Dense benchmark datasets.
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.