CHEER: Rich Model Helps Poor Model via Knowledge Infusion

Cao Xiao, Trong Nghia Hoang, Shenda Hong, Tengfei Ma, Jimeng Sun

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)531-543
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume34
Issue number2
DOIs
StatePublished - Feb 1 2022

Keywords

  • embedding
  • Health analytics
  • representation learning

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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