# GRACE: Graph Neural Networks for Locus-of-Care Prediction under Extreme Class Imbalance

arXiv: https://arxiv.org/abs/2510.20671 · HTML: https://mlsanity.com/paper/2510.20671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [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)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [GraphGPT: Graph Instruction Tuning for Large Language Models](https://mlsanity.com/paper/2310.13023/) (2023)
- [Empath: Understanding Topic Signals in Large-Scale Text](https://mlsanity.com/paper/1602.06979/) (2016)
- [GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks](https://mlsanity.com/paper/2406.12925/) (2024)
- [Subsampling Graphs with GNN Performance Guarantees](https://mlsanity.com/paper/2502.16703/) (2025)
- [Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media](https://mlsanity.com/paper/2504.12355/) (2025)
- [Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks](https://mlsanity.com/paper/2406.17073/) (2024)
- [DeepJ: Graph Convolutional Transformers with Differentiable Pooling for Patient Trajectory Modeling](https://mlsanity.com/paper/2506.15809/) (2025)

## Abstract (arXiv)

Determining the appropriate locus of care for addiction patients is one of the most critical clinical decisions that affects patient treatment outcomes and effective use of resources. With a lack of sufficient specialized treatment resources, such as inpatient beds or staff, there is an unmet need to develop an automated framework for the same. Current decision-making approaches suffer from severe class imbalances in addiction datasets. To address this limitation, we propose a novel graph neural network (GRACE) framework that formalizes locus of care prediction as a structured learning problem. In addition, we propose a new approach of obtaining an unbiased meta-graph to train a GNN to overcome the class imbalance problem. Experimental results with real-world data show an improvement of 11-35% in terms of the F1 score of the minority class over competitive baselines. Further, if we jointly finetune the base embedding fed into GRACE as input together with the rest of the GNN component of GRACE, there is a remarkable boost of 15.8% in performance.