Context-Aware adaptive normalization LSTM (CAAN-LSTM) for immunotherapy decision support in cancer clinical data...

Area of Science:

• Computational biology
• Artificial intelligence in medicine
• Biomedical data science

Background:

• Cancer immunotherapy clinical decision-making faces challenges with heterogeneous and incomplete patient time-series data.
• Traditional models often fail due to static normalization and poor handling of missing data, limiting real-world performance.
• Individual patient variability is a key factor that complicates accurate treatment prediction.

Purpose of the Study:

• To introduce CAAN-LSTM, a novel deep learning architecture for enhanced clinical time-series data analysis.
• To address limitations in current models regarding patient variability, data incompleteness, and normalization strategies.
• To improve the accuracy and adaptability of predictive models for cancer immunotherapy.

Main Methods:

• Developed CAAN-LSTM, a deep learning model with a meta-learned adaptive normalization layer and attention mechanisms.
• Incorporated a hypernetwork for personalized scaling/shifting parameters and a transformer encoder for static patient features.
• Implemented a learned masking strategy for missing values and quantization-aware training for efficient deployment.

Main Results:

• CAAN-LSTM demonstrated superior prediction accuracy compared to traditional models, especially with high data variability or missing values.
• The model was successfully piloted in Saudi medical facilities and validated on real-world clinical datasets.
• Showcased significant potential for assisting in individualized cancer treatment planning.

Conclusions:

• CAAN-LSTM provides a robust and adaptive framework for modeling complex clinical time-series data.
• The integration of patient-specific context and dynamic normalization enhances decision support for immunotherapy.
• The model is well-suited for practical clinical implementation, improving treatment planning and patient outcomes.