activity
20202025
most citedPrompt Engineering for Healthcare: Methodologies and Applications

118 citations · 236 across the 20 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.AI2023★ 2 cited

Hierarchical Pretraining on Multimodal Electronic Health Records

Xiaochen Wang, Junyu Luo, Jiaqi Wang +5

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 domai…

cs.LG2023★ 2 cited

MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation

Yuan Zhong, Suhan Cui, Jiaqi Wang +7

Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in…

cs.LG2023★ 13 cited

Towards Personalized Federated Learning via Heterogeneous Model Reassembly

Jiaqi Wang, Xingyi Yang, Suhan Cui +4

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures.…

cs.AI2023★ 118 cited

Prompt Engineering for Healthcare: Methodologies and Applications

Jiaqi Wang, Enze Shi, Sigang Yu +21

Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aim…

cs.LG2023★ 3 cited

Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT

Jiaqi Wang, Shenglai Zeng, Zewei Long +3

Federated learning (FL) enables multiple clients to train models collaboratively without sharing local data, which has achieved promising results in different areas, including the…