Phenome-wide causal proteomics enhance systemic lupus erythematosus flare prediction: A study in Asian populations

Autor: Chen, Liying, Deng, Ou, Fang, Ting, Chen, Mei, Zhang, Xvfeng, Cong, Ruichen, Lu, Dingqi, Zhang, Runrun, Jin, Qun, Wang, Xinchang

Objective: Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by unpredictable flares. This study aimed to develop a novel proteomics-based risk prediction model specifically for Asian SLE populations to enhance personal

Externí odkaz: http://arxiv.org/abs/2411.11915

Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-Attention

Autor: Deng, Ou, Jin, Qun

Addressing missing data in complex datasets including electronic health records (EHR) is critical for ensuring accurate analysis and decision-making in healthcare. This paper proposes dynamically adaptable structural equation modeling (SEM) using a s

Externí odkaz: http://arxiv.org/abs/2308.12388

Policy-Based Reinforcement Learning for Assortative Matching in Human Behavior Modeling

Autor: Deng, Ou, Jin, Qun

Publikováno v: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14029. Springer, Cham

This paper explores human behavior in virtual networked communities, specifically individuals or groups' potential and expressive capacity to respond to internal and external stimuli, with assortative matching as a typical example. A modeling approac

Externí odkaz: http://arxiv.org/abs/2211.03936

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Multiple feature selection based on an optimization strategy for causal analysis of health data.

Autor: Cong, Ruichen, Deng, Ou, Nishimura, Shoji, Ogihara, Atsushi, Jin, Qun

Publikováno v: Health Information Science & Systems; 11/12/2024, Vol. 12 Issue 1, p1-12, 12p