Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction

Christian Witte, Philipp Cimiano


Abstract
We present a deep learning based information extraction system that can extract the design and results of a published abstract describing a Randomized Controlled Trial (RCT). In contrast to other approaches, our system does not regard the PICO elements as flat objects or labels but as structured objects. We thus model the task as the one of filling a set of templates and slots; our two-step approach recognizes relevant slot candidates as a first step and assigns them to a corresponding template as second step, relying on a learned pairwise scoring function that models the compatibility of the different slot values. We evaluate the approach on a dataset of 211 manually annotated abstracts for type 2 Diabetes and Glaucoma, showing the positive impact of modelling intra-template entity compatibility. As main benefit, our approach yields a structured object for every RCT abstract that supports the aggregation and summarization of clinical trial results across published studies and can facilitate the task of creating a systematic review or meta-analysis.
Anthology ID:
2022.bionlp-1.18
Volume:
Proceedings of the 21st Workshop on Biomedical Language Processing
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
178–192
Language:
URL:
https://aclanthology.org/2022.bionlp-1.18
DOI:
10.18653/v1/2022.bionlp-1.18
Bibkey:
Cite (ACL):
Christian Witte and Philipp Cimiano. 2022. Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 178–192, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction (Witte & Cimiano, BioNLP 2022)
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PDF:
https://aclanthology.org/2022.bionlp-1.18.pdf
Video:
 https://aclanthology.org/2022.bionlp-1.18.mp4