Typed Markers and Context for Clinical Temporal Relation Extraction.

Cheng, C, Weiss JC

To appear in the Conference on Machine Learning for Health, Proceedings of Machine Learning Research (PMLR), 2023.

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Abstract:

Reliable extraction of temporal relations from clinical notes is a growing need in many clin- ical research domains. Our work introduces typed markers to the task of clinical temporal relation extraction. We demonstrate that the addition of medical entity information to clinical text as tags with context sentences then input to a transformer-based architecture can outperform more complex systems requiring feature engineering and temporal reason- ing. We propose several strategies of typed marker creation that incorporate entity type information at different granularities, with extensive experiments to test their effectiveness. Our system establishes the best result on I2B2, a clinical benchmark dataset for temporal relation extraction, with a F1 at 83.5% that provides a substantial 3.3% improvement over the previous best system.

Cheng, C, Weiss JC. Typed Markers and Context for Clinical Temporal Relation Extraction. 
To appear in the Conference on Machine Learning for Health, Proceedings of Machine Learning Research (PMLR), 2023.

URL: https://static1.squarespace.com/static/59d5ac1780bd5ef9c396eda6/t/64d198242588467b55e5b7e0/1691457572386/ID23_Research+Paper_2023.pdf