EnSidNet: Enhanced Hybrid Siamese-Deep Network for grouping clinical trials into drug-development pathways

Lucia Pagani


Abstract
Siamese Neural Networks have been widely used to perform similarity classification in multi-class settings. Their architecture can be used to group the clinical trials belonging to the same drug-development pathway along the several clinical trial phases. Here we present an approach for the unmet need of drug-development pathway reconstruction, based on an Enhanced hybrid Siamese-Deep Neural Network (EnSidNet). The proposed model demonstrates significant improvement above baselines in a 1-shot evaluation setting and in a classical similarity setting. EnSidNet can be an essential tool in a semi-supervised learning environment: by selecting clinical trials highly likely to belong to the same drug-development pathway it is possible to speed up the labelling process of human experts, allowing the check of a consistent volume of data, further used in the model’s training dataset.
Anthology ID:
2021.naacl-main.24
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
254–266
Language:
URL:
https://aclanthology.org/2021.naacl-main.24
DOI:
10.18653/v1/2021.naacl-main.24
Bibkey:
Cite (ACL):
Lucia Pagani. 2021. EnSidNet: Enhanced Hybrid Siamese-Deep Network for grouping clinical trials into drug-development pathways. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 254–266, Online. Association for Computational Linguistics.
Cite (Informal):
EnSidNet: Enhanced Hybrid Siamese-Deep Network for grouping clinical trials into drug-development pathways (Pagani, NAACL 2021)
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PDF:
https://aclanthology.org/2021.naacl-main.24.pdf
Video:
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