Predicting Chemotherapy-Related Symptom Deterioration Using Hybrid Deep Learning Architecture

Predicting symptom escalation in chemotherapy patients is essential for proactive intervention and improved clinical outcomes. This study leverages hybrid deep learning architectures, specifically Convolutional Neural Networks with Long Short-Term Memory (CNN-LSTM), to forecast the progression of 12 self-reported symptoms, categorized into physical (e.g., nausea, fatigue, pain) and mental (e.g., anxiety, cognitive impairment, mood changes) groups. The dataset consists of daily self-reported symptom logs from individuals undergoing chemotherapy. Given the high class imbalance-where 84% of cases showed no escalation-symptom data were aggregated into intervals of 3 to 7 days to improve predictive performance and temporal resolution. The CNN-LSTM model combines convolutional layers for extracting patterns within a local time window with LSTM layers for capturing long-term temporal dependencies. The model was trained using five-fold cross-validation to ensure robustgeneralization. Results indicate that 5-day intervals yielded the highest predictive accuracy for physical symptom prediction, with the CNN-LSTM model achieving an accuracy of 83%, precision of 89%, recall of 86%, F1-score of 88%, and an AUCof 83%. These findings highlight the effectiveness of hybrid deep learning architectures in symptom monitoring and early detection, enabling AI-driven decision support for real-time clinical interventions. Integrating these models into digital health systems could facilitate continuous symptom tracking, enhance predictive accuracy, and improve the quality of care for chemotherapy patients.