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
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.naacl-main.24.pdf