Frailty prediction in patients with chronic digestive system diseases: based on multi-task learning model - Hu, Sihan; Guo, Xiaochuan; Wang, Xiaobao; Jin, Zixiang; Zhou, Chenyang; Tu, Lang; Shi, Zhoulong; Ao, Weiyi; Zhang, Xin; Zheng, Jay; Zhang, Xuezhi; Ye, Hui (Frontiers in Medicine, 2025)
BackgroundChronic digestive system diseases (CDSD) pose a major health challenge worldwide, significantly increasing morbidity and mortality rates. The frailty index is crucial for assessing patient prognosis. To address the need for proactive healthcare, we developed a multi-timepoint frailty prediction model.MethodsThis study collected data from 565 patients with CDSD, including their frailty assessments at 3 and 6 years of follow-up. Utilizing the Multi-Gate Mixture-of-Experts (MMoE) framework, we built and evaluated five models: Tab Transformer, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Extreme Gradient Boosting (XGBoost) and Random Forest (RF). We comprehensively compared the predictive capabilities of these models on both validation and test sets.ResultsThe MMoE framework consistently outperforms single models in predicting both 3-year and 6-year frailty indices across most metrics. Specifically, for 3-year predictions, the single model achieves an accuracy of 0.9801 (95% CI: 0.963–0.990) on the train set and 0.5487 (95% CI: 0.457–0.637) on the test set, while the MMoE model reaches 0.956 (95% CI: 0.933–0.971) and 0.982 (95% CI: 0.938–0.995), respectively. The RF model demonstrated perfect performance, with Micro-AUC values of 1.000 in both training and test sets for both 3-year and 6-year intervals, leading other models in terms of accuracy, precision, recall, F1 score. The Tab Transformer model achieved high Micro-AUC values across all prediction intervals, with values of 0.997 and 0.995 in the training set for 3-year and 6-year predictions, respectively, and corresponding test set values of 0.999 and 0.987.ConclusionThis MMoE-based approach can predict frailty at key time points, offering insights into frailty progression and aiding clinical decision making. Integrating this AI model into CDSD management can promote early interventions and personalized treatment plans.