Self-Supervised Intermediate Fine-Tuning of Biomedical Language Models for Interpreting Patient Case Descriptions

Israa Alghanmi, Luis Espinosa-Anke, Steven Schockaert


Abstract
Interpreting patient case descriptions has emerged as a challenging problem for biomedical NLP, where the aim is typically to predict diagnoses, to recommended treatments, or to answer questions about cases more generally. Previous work has found that biomedical language models often lack the knowledge that is needed for such tasks. In this paper, we aim to improve their performance through a self-supervised intermediate fine-tuning strategy based on PubMed abstracts. Our solution builds on the observation that many of these abstracts are case reports, and thus essentially patient case descriptions. As a general strategy, we propose to fine-tune biomedical language models on the task of predicting masked medical concepts from such abstracts. We find that the success of this strategy crucially depends on the selection of the medical concepts to be masked. By ensuring that these concepts are sufficiently salient, we can substantially boost the performance of biomedical language models, achieving state-of-the-art results on two benchmarks.
Anthology ID:
2022.coling-1.123
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1432–1441
Language:
URL:
https://aclanthology.org/2022.coling-1.123
DOI:
Bibkey:
Cite (ACL):
Israa Alghanmi, Luis Espinosa-Anke, and Steven Schockaert. 2022. Self-Supervised Intermediate Fine-Tuning of Biomedical Language Models for Interpreting Patient Case Descriptions. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1432–1441, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Self-Supervised Intermediate Fine-Tuning of Biomedical Language Models for Interpreting Patient Case Descriptions (Alghanmi et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.123.pdf
Code
 israa-alghanmi/intermediate-ft-biomedical-lms
Data
DisKnEHeadQA