  There is a growing interest in applying deep learning (DL) to healthcare,
driven by the availability of data with multiple feature channels in rich-data
environments (e.g., intensive care units). However, in many other practical
situations, we can only access data with much fewer feature channels in a
poor-data environments (e.g., at home), which often results in predictive
models with poor performance. How can we boost the performance of models
learned from such poor-data environment by leveraging knowledge extracted from
existing models trained using rich data in a related environment? To address
this question, we develop a knowledge infusion framework named CHEER that can
succinctly summarize such rich model into transferable representations, which
can be incorporated into the poor model to improve its performance. The infused
model is analyzed theoretically and evaluated empirically on several datasets.
Our empirical results showed that CHEER outperformed baselines by 5.60% to
46.80% in terms of the macro-F1 score on multiple physiological datasets.
