Attentive Multiview Text Representation for Differential Diagnosis

Hadi Amiri, Mitra Mohtarami, Isaac Kohane


Abstract
We present a text representation approach that can combine different views (representations) of the same input through effective data fusion and attention strategies for ranking purposes. We apply our model to the problem of differential diagnosis, which aims to find the most probable diseases that match with clinical descriptions of patients, using data from the Undiagnosed Diseases Network. Our model outperforms several ranking approaches (including a commercially-supported system) by effectively prioritizing and combining representations obtained from traditional and recent text representation techniques. We elaborate on several aspects of our model and shed light on its improved performance.
Anthology ID:
2021.acl-short.128
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1012–1019
Language:
URL:
https://aclanthology.org/2021.acl-short.128
DOI:
10.18653/v1/2021.acl-short.128
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-short.128.pdf
Optional supplementary material:
 2021.acl-short.128.OptionalSupplementaryMaterial.pdf