arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
By Nikkie Hooman, Zhongjie Wu, Eric C. Larson, Mehak Gupta
arXiv:2603.08459v2 Announce Type: replace
Abstract: Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach to improving trus...
By L. Juli\'an Lechuga L\'opez, Tim G. J. Rudner, Farah E. Shamout
The paper presents a newly curated, multi-center, multi-modal, and longitudinal lung cancer dataset comprising 1,365 patients with whole-slide images, CT scans, PET scans, structured clinical data, transcriptomics, and follow-up information. The dataset features substantial, non-uniform missingness across modalities, making it ideal for evaluating robust multi-modal fusion strategies. Benchmarks on 12‑month overall survival, disease‑specific survival, and longitudinal hazard prediction demonstrate that integrating complementary modalities consistently outperforms uni-modal approaches, even under severe missing data.
By Rita Cordeiro Mendes, Maria Rita Fonseca Verdelho, Carlos Santiago, Catarina Barata
arXiv:2607. 18270v1 Announce Type: new Abstract: While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge.
By Kyunghoon Jeon, Youmin Ko, Woohwan Jung, Hyunjoon Kim
The paper introduces CFKD-AFN, a cross‑fidelity knowledge distillation and adaptive fusion network that uses abundant low‑fidelity simulation data to improve personalized treatment outcome predictions from scarce high‑fidelity trial data. The dual‑channel distillation module extracts complementary knowledge from the low‑fidelity model, while an attention‑guided fusion module adaptively integrates multi‑source information. Experiments on chronic obstructive pulmonary disease data demonstrate significant reductions in mean squared error (6.67%–74.55%) and mean absolute percentage error (1.43%–51.54%) compared to competing methods, and the framework can be extended to an interpretable variant for feature‑attribution analysis.
By Wenjie Chen, Li Zhuang, Ziying Luo, Yu Liu, Jiahao Wu, Shengcai Liu
arXiv:2609.38924v1 Announce Type: new
Abstract: Major adverse cardiovascular events (MACE) remain the leading cause of mortality worldwide. Opportunistic screening using routinely acquired clinical d...
By Jialu Pi, Yanan Ma, Weijie Chen, Owen Crystal, Shubham Trivedi, Stephen Xie, Anna Silverman, Matthew Stib, Chadi Ayoub, Reza Arsanjani, Imon Banerjee