52 citations · 65 across the 5 of their papers we have counts for
6 papers
When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusion
Kejing Yin, Haizhou Xu, Wenfang Yao +5
Machine learning holds promise for advancing clinical decision support, yet it remains unclear when multimodal learning truly helps in practice, particularly under modality missing…
Multimodal Disease Progression Modeling via Spatiotemporal Disentanglement and Multiscale Alignment
Chen Liu, Wenfang Yao, Kejing Yin +2
Longitudinal multimodal data, including electronic health records (EHR) and sequential chest X-rays (CXRs), is critical for modeling disease progression, yet remains underutilized…
Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation
Wenfang Yao, Chen Liu, Kejing Yin +2
Integrating multi-modal clinical data, such as electronic health records (EHR) and chest X-ray images (CXR), is particularly beneficial for clinical prediction tasks. However, in a…
DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency
Wenfang Yao, Kejing Yin, William K. Cheung +2
The combination of electronic health records (EHR) and medical images is crucial for clinicians in making diagnoses and forecasting prognosis. Strategically fusing these two data m…
Learning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health Records
Kejing Yin, William K. Cheung, Benjamin C. M. Fung +1
Non-negative tensor factorization has been shown a practical solution to automatically discover phenotypes from the electronic health records (EHR) with minimal human supervision.…
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors
Ardavan Afshar, Kejing Yin, Sherry Yan +4
Existing tensor factorization methods assume that the input tensor follows some specific distribution (i.e. Poisson, Bernoulli, and Gaussian), and solve the factorization by minimi…