# Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies

arXiv: https://arxiv.org/abs/2408.15294 · HTML: https://mlsanity.com/paper/2408.15294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning](https://mlsanity.com/paper/2109.00840/) (2021)
- [An Information Extraction Study: Take In Mind the Tokenization!](https://mlsanity.com/paper/2303.15100/) (2023)
- [Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare](https://mlsanity.com/paper/2305.05640/) (2023)
- [Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation](https://mlsanity.com/paper/2305.08139/) (2023)

## Abstract (arXiv)

Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standardization or sparseness of the data. We previously introduced a person-centric ontology to organize information about individual patients, and a representation learning framework to extract person-centric knowledge graphs (PKGs) and to train Graph Neural Networks (GNNs). In this paper, we propose a systematic approach to examine the results of GNN models trained with both structured and unstructured information from the MIMIC-III dataset. Through ablation studies on different clinical, demographic, and social data, we show the robustness of this approach in identifying predictive features in PKGs for the task of readmission prediction.