Predicting Avatrombopag response in children with immune thrombocytopenia: A multi-view learning framework for tabular missing data.

BACKGROUND AND

OBJECTIVE

Avatrombopag is a thrombopoietin receptor agonist that has demonstrated clinical efficacy in rapidly increasing platelet counts and achieving bleeding control in children with chronic immune thrombocytopenia. Early prediction of treatment response is critical for reducing unnecessary drug exposure and guiding personalized treatment strategies. However, accurate prediction remains challenging due to the inherent characteristics of clinical tabular data, including missing values caused by inconsistent data recording, small sample size, and high-dimensional clinical features. This study aims to develop an effective computational framework for early prediction of treatment response under these conditions.

METHODS

We propose a multi-view learning framework that constructs multiple views by applying diverse imputation methods to the same dataset, with each view capturing unique assumptions about missingness. A feature selection encoder is employed to reduce feature redundancy and improve model interpretability. Multi-view fusion and co-regularization are integrated at the prediction level to learn complementary patterns across different views. In addition, contrastive learning is introduced to alleviate the small data problem and enhance representation robustness.

RESULTS

Experiments on real-world clinical data of children treated with avatrombopag demonstrate that the proposed method consistently outperforms multiple competitive baseline models in predicting treatment response.

CONCLUSIONS

This study provides a practical and interpretable multi-view learning framework for early identification of treatment response in chronic immune thrombocytopenia, supporting more personalized and efficient treatment decisions in pediatric hematology.