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Jun Fu

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EAAI Journal 2026 Journal Article

Dynamic patient similarity modeling with multi-source fused clinical knowledge for enhanced disease prediction

Yichen He
Yuchen Lin
Xiaorou Zheng
Shoubin Dong
Jun Fu

Accurate clinical disease prediction is crucial for modern healthcare. However, current methods for clinical disease prediction using multi-source Electronic Health Records (EHRs) are limited by coarse-grained integration and static utilization of similar patient data, failing to capture inter-entity interactions or dynamic disease evolution. In terms of the contribution to artificial intelligence, this paper proposes MFaDP, a novel framework integrating multi-source clinical knowledge including external knowledge graphs, medical code hierarchies, and internal co-occurrence patterns to construct multi-dimensional knowledge subgraphs, and pre-train high-quality entity representations via self-supervised reconstruction tasks for patient modeling and disease prediction. Crucially, MFaDP pioneers the dynamic modeling of similar patient evolution, leveraging historical visit records with the clinical pathways of similar patients, with an attention mechanism to dynamically extract reference information and uncover latent pathological patterns. Regarding the application in engineering, the proposed framework is applied to the critical task of intelligent clinical decision support. Extensive experiments on two large-scale public datasets, MIMIC-III and MIMIC-IV, demonstrate that MFaDP significantly outperforms state-of-the-art models across multiple prediction tasks, validating its advanced capability in harnessing complex EHR data for enhanced predictive performance.

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AAAI Conference 2021 Conference Paper

Consistent-Separable Feature Representation for Semantic Segmentation

Xingjian He
Jing Liu
Jun Fu
Xinxin Zhu
Jinqiao Wang
Hanqing Lu

Cross-entropy loss combined with softmax is one of the most commonly used supervision components in most existing segmentation methods. The softmax loss is typically good at optimizing the inter-class difference, but not good at reducing the intra-class variation, which can be suboptimal for semantic segmentation task. In this paper, we propose a Consistent-Separable Feature Representation Network to model the Consistent-Separable (C-S) features, which are intra-class consistent and inter-class separable, improving the discriminative power of the deep features. Specifically, we develop a Consistent-Separable Feature Learning Module to obtain C-S features through a new loss, called Class-Aware Consistency loss. This loss function is proposed to force the deep features to be consistent among the same class and apart between different classes. Moreover, we design an Adaptive feature Aggregation Module to fuse the C-S features and original features from backbone for the better semantic prediction. We show that compared with various baselines, the proposed method brings consistent performance improvement. Our proposed approach achieves state-of-the-art performance on Cityscapes (82. 6% mIoU in test set), ADE20K (46. 65% mIoU in validation set), COCO Stuff (41. 3% mIoU in validation set) and PASCAL Context (55. 9% mIoU in test set).

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