Typed Markers and Context for Clinical Temporal Relation Extraction

Cheng Cheng, Jeremy C. Weiss
Proceedings of the 8th Machine Learning for Healthcare Conference, PMLR 219:94-109, 2023.

Abstract

Reliable extraction of temporal relations from clinical notes is a growing need in many clinical 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 reasoning. 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v219-cheng23a, title = {Typed Markers and Context for Clinical Temporal Relation Extraction}, author = {Cheng, Cheng and Weiss, Jeremy C.}, booktitle = {Proceedings of the 8th Machine Learning for Healthcare Conference}, pages = {94--109}, year = {2023}, editor = {Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton, Zachary and Ranganath, Rajesh and Urteaga, Iñigo and Yeung, Serene}, volume = {219}, series = {Proceedings of Machine Learning Research}, month = {11--12 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v219/cheng23a/cheng23a.pdf}, url = {https://proceedings.mlr.press/v219/cheng23a.html}, abstract = {Reliable extraction of temporal relations from clinical notes is a growing need in many clinical 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 reasoning. 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.} }
Endnote
%0 Conference Paper %T Typed Markers and Context for Clinical Temporal Relation Extraction %A Cheng Cheng %A Jeremy C. Weiss %B Proceedings of the 8th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2023 %E Kaivalya Deshpande %E Madalina Fiterau %E Shalmali Joshi %E Zachary Lipton %E Rajesh Ranganath %E Iñigo Urteaga %E Serene Yeung %F pmlr-v219-cheng23a %I PMLR %P 94--109 %U https://proceedings.mlr.press/v219/cheng23a.html %V 219 %X Reliable extraction of temporal relations from clinical notes is a growing need in many clinical 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 reasoning. 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.
APA
Cheng, C. & Weiss, J.C.. (2023). Typed Markers and Context for Clinical Temporal Relation Extraction. Proceedings of the 8th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 219:94-109 Available from https://proceedings.mlr.press/v219/cheng23a.html.

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