Knowledge-aware Attention Network for Medication Effectiveness Prediction

Yingying Zhang, Xian Wu, Yu Zhang, Yefeng Zheng


Abstract
The first 24 hours’ medication plan is critical to patients with serious or life-threatening illnesses and injuries. An appropriate medication can result in a lower mortality, a shorter length stay and a higher APACHE score. However, in clinical practice, the medication plan is often error-prone, especially when a decision must be made quickly for life-threatening situations in Intensive Care Unit (ICU). Therefore, predicting the effectiveness of the first 24 hours’ medication plan is of great importance in assisting doctors to make proper decisions. Existing effectiveness prediction works usually focus on one specific medicine, one specific disease, or one specific lab test, making it hard to extend to general medicines and diseases in hospital/ICU scenarios. In this paper, we propose to predict medication effectiveness of the first 24 hours in hospital/ICU based on patients’ information. Specifically, we use a knowledge enhanced module to incorporate external knowledge about medications and a medical feature learning module to determine the interaction between diagnosis and medications. To handle the data imbalance problem, we further optimize the proposed model with a contrastive loss. Extensive experimental results on a public dataset show that our model can significantly outperform state-of-the-art methods.
Anthology ID:
2024.lrec-main.856
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
9799–9809
Language:
URL:
https://aclanthology.org/2024.lrec-main.856
DOI:
Bibkey:
Cite (ACL):
Yingying Zhang, Xian Wu, Yu Zhang, and Yefeng Zheng. 2024. Knowledge-aware Attention Network for Medication Effectiveness Prediction. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9799–9809, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Knowledge-aware Attention Network for Medication Effectiveness Prediction (Zhang et al., LREC-COLING 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/2024.lrec-main.856.pdf