CHEER: Rich Model Helps Poor Model via Knowledge Infusion

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 percent in terms of the macro-F1 score on multiple physiological datasets.