# MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering

arXiv: https://arxiv.org/abs/2204.10011 · HTML: https://mlsanity.com/paper/2204.10011/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [Attend and Diagnose: Clinical Time Series Analysis using Attention Models](https://mlsanity.com/paper/1711.03905/) (2017)
- [ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context](https://mlsanity.com/paper/1911.12216/) (2019)
- [StageNet: Stage-Aware Neural Networks for Health Risk Prediction](https://mlsanity.com/paper/2001.10054/) (2020)
- [MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records](https://mlsanity.com/paper/1905.03218/) (2019)
- [Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare](https://mlsanity.com/paper/2105.07542/) (2021)

## Abstract (arXiv)

In healthcare prediction tasks, it is essential to exploit the correlations between medical features and learn better patient health representations. Existing methods try to estimate feature correlations only from data, or increase the quality of estimation by introducing task-specific medical knowledge. However, such methods either are difficult to estimate the feature correlations due to insufficient training samples, or cannot be generalized to other tasks due to reliance on specific knowledge. There are medical research revealing that not all the medical features are strongly correlated. Thus, to address the issues, we expect to group up strongly correlated features and learn feature correlations in a group-wise manner to reduce the learning complexity without losing generality. In this paper, we propose a general patient health representation learning framework MedFACT. We estimate correlations via measuring similarity between temporal patterns of medical features with kernel methods, and cluster features with strong correlations into groups. The feature group is further formulated as a correlation graph, and we employ graph convolutional networks to conduct group-wise feature interactions for better representation learning. Experiments on two real-world datasets demonstrate the superiority of MedFACT. The discovered medical findings are also confirmed by literature, providing valuable medical insights and explanations.