Abstract
Diagnosis prediction on admission notes is a core clinical task. However, these notes may incompletely describe the patient. Also, clinical language models may suffer from idiosyncratic language or imbalanced vocabulary for describing diseases or symptoms. We tackle the task of diagnosis prediction, which consists of predicting future patient diagnoses from clinical texts at the time of admission. We improve the performance on this task by introducing an additional signal from support sets of diagnostic codes from prior admissions or as they emerge during differential diagnosis. To enhance the robustness of diagnosis prediction methods, we propose to augment clinical text with potentially complementary set data from diagnosis codes from previous patient visits or from codes that emerge from the current admission as they become available through diagnostics. We discuss novel attention network architectures and augmentation strategies to solve this problem. Our experiments reveal that support sets improve the performance drastically to predict less common diagnosis codes. Our approach clearly outperforms the previous state-of-the-art PubMedBERT baseline by up 3% points. Furthermore, we find that support sets drastically improve the performance for pregnancy- and gynecology-related diagnoses up to 32.9% points compared to the baseline.- Anthology ID:
- 2022.coling-1.422
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4765–4775
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.422
- DOI:
- Cite (ACL):
- Paul Grundmann, Tom Oberhauser, Felix Gers, and Alexander Löser. 2022. Attention Networks for Augmenting Clinical Text with Support Sets for Diagnosis Prediction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4765–4775, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Attention Networks for Augmenting Clinical Text with Support Sets for Diagnosis Prediction (Grundmann et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.coling-1.422.pdf
- Data
- MIMIC-III