# Learning Representations of Missing Data for Predicting Patient Outcomes

arXiv: https://arxiv.org/abs/1811.04752 · HTML: https://mlsanity.com/paper/1811.04752/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Predicting Patient Outcomes with Graph Representation Learning](https://mlsanity.com/paper/2101.03940/) (2021)
- [Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks](https://mlsanity.com/paper/2305.10544/) (2023)
- [Review for Handling Missing Data with special missing mechanism](https://mlsanity.com/paper/2404.04905/) (2024)
- [Investigating the Interplay between Features and Structures in Graph Learning](https://mlsanity.com/paper/2308.09570/) (2023)
- [Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease](https://mlsanity.com/paper/2406.14442/) (2024)

## Abstract (arXiv)

Extracting actionable insight from Electronic Health Records (EHRs) poses several challenges for traditional machine learning approaches. Patients are often missing data relative to each other; the data comes in a variety of modalities, such as multivariate time series, free text, and categorical demographic information; important relationships among patients can be difficult to detect; and many others. In this work, we propose a novel approach to address these first three challenges using a representation learning scheme based on message passing. We show that our proposed approach is competitive with or outperforms the state of the art for predicting in-hospital mortality (binary classification), the length of hospital visits (regression) and the discharge destination (multiclass classification).