Junyu Luo


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Hierarchical Pretraining on Multimodal Electronic Health Records
Xiaochen Wang | Junyu Luo | Jiaqi Wang | Ziyi Yin | Suhan Cui | Yuan Zhong | Yaqing Wang | Fenglong Ma
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downstream tasks using a single pretrained model. To tackle this challenge, this paper introduces a novel, general, and unified pretraining framework called MedHMP, specifically designed for hierarchically multimodal EHR data. The effectiveness of the proposed MedHMP is demonstrated through experimental results on eight downstream tasks spanning three levels. Comparisons against eighteen baselines further highlight the efficacy of our approach.


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Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation
Junyu Luo | Junxian Lin | Chi Lin | Cao Xiao | Xinning Gui | Fenglong Ma
Proceedings of the 29th International Conference on Computational Linguistics

Patients with low health literacy usually have difficulty understanding medical jargon and the complex structure of professional medical language. Although some studies are proposed to automatically translate expert language into layperson-understandable language, only a few of them focus on both accuracy and readability aspects simultaneously in the clinical domain. Thus, simplification of the clinical language is still a challenging task, but unfortunately, it is not yet fully addressed in previous work. To benchmark this task, we construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches. Besides, we propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance compared with eight strong baselines. To fairly evaluate the performance, we also propose three specific evaluation metrics. Experimental results demonstrate the utility of the annotated MedLane dataset and the effectiveness of the proposed model DECLARE.


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Fusion: Towards Automated ICD Coding via Feature Compression
Junyu Luo | Cao Xiao | Lucas Glass | Jimeng Sun | Fenglong Ma
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021