Technology advancements have enabled a wealth of health information to be remotely collected, resulting in increasing use of telemonitoring for patients with chronic diseases. In particular, multi-channel bio-signals such as ECG and EEG, gold-standard diagnostic approaches for many diseases, are able to be collected at home. To utilize multi-channel bio-signals for telemonitoring and telemedicine, it is critical to develop a rigorous prediction model that "translates"the monitored symptomatic signals into a clinical indicator of disease severity to facilitate disease monitoring and diagnosis. However, multi-level and multi-channel data pose major challenges for most statistical prediction methods. To address these challenges, this paper proposes a multi-level multi-channel framework to integrate multi-channel epoch-level bio-signals and other patient-level health covariates for precise prediction of disease severity in health telemonitoring. The proposed framework was applied in a real-world dataset consisting of 409 patients and achieved the highest prediction accuracy in comparison with benchmark methods that do not take account of the multi-level structure of features into prediction. The findings of this study demonstrate the efficacy and performance of the proposed method in predicting disease severity from multi-level multi-channel features, which contributes a novel predictive analytical tool to facilitate bio-signal-based health telemonitoring.