Yuntian Wang: Multi-View Learning for Predicting Avatrombopag Response in Pediatric Immune Thrombocytopenia
Yuntian Wang, Assistant Engineer at Chinese Academy of Sciences, shared a post on LinkedIn about a recent article he and his colleagues co-authored, published in Computer Methods and Programs in Biomedicine, adding:
“I’m pleased to share that our paper, ‘Predicting avatrombopag response in children with immune thrombocytopenia: A multi-view learning framework for tabular missing data,’has been published in Computer Methods and Programs in Biomedicine.
This study focuses on a practical challenge in clinical AI: how to build reliable predictive models from small, heterogeneous, and incomplete clinical tabular datasets.
Using pediatric immune thrombocytopenia as the clinical context, we developed a multi-view learning framework to predict avatrombopag treatment response, aiming to better support individualized therapeutic decision-making.
Beyond this specific clinical task, the work also explores a broader methodological issue: how deep learning models can be adapted to real-world clinical data, where missingness, limited sample size, and feature heterogeneity are often unavoidable.
I am grateful to all co-authors and collaborators for their valuable contributions to this interdisciplinary work.”
Title: Predicting Avatrombopag response in children with immune thrombocytopenia: A multi-view learning framework for tabular missing data
Authors: Yuntian Wang, Yongqiang Tang, Xiaoling Cheng, Xi Lin, Zhenping Chen, Runhui Wu, Wensheng Zhang
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