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BiSAUM: Bi-Directional Sparse Attention Transformer for Cancer Cell Prediction in Multi-Domain Sustainable Healthcare

Xing Liu, Byung-Gyu Kim, Jianhui Lv

IEEE Journal of Biomedical and Health Informatics

|December 10, 2025

Summary

This summary is machine-generated.

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BiSAUM, a novel framework, efficiently analyzes sustainable healthcare and cancer cell patterns. It improves predictions and classifications, aiding evidence-based cancer treatment decisions.

Area of Science:

Computational biology
Health informatics
Biomedical data analysis

Background:

Understanding the intricate relationship between healthcare systems and cancer progression is crucial for effective treatment.
Existing computational models often struggle to balance accuracy with efficiency when analyzing complex multi-domain data.

Purpose of the Study:

To introduce BiSAUM, a bi-directional sparse attention transformer, for analyzing sustainable healthcare patterns and cancer cell characteristics.
To enhance computational efficiency while maintaining high performance in analyzing these interconnected domains.

Main Methods:

Development of a novel bi-directional sparse selection mechanism within the transformer architecture.
Simultaneous analysis of sustainable healthcare delivery systems and cancer cell behaviors.

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Experimental validation against state-of-the-art baselines.

Main Results:

BiSAUM achieved 12.1% lower Mean Squared Error (MSE) in healthcare system prediction.
Demonstrated 4.4% higher accuracy in cancer pattern classification.
Reduced computational time by 35.8% and memory usage by 32.6%.

Conclusions:

BiSAUM offers a computationally efficient and high-performing solution for analyzing multi-domain healthcare and cancer data.
The framework advances the understanding of how sustainable healthcare impacts cancer progression.
Enables more effective, evidence-based medical decisions in cancer prevention and treatment.