# Machine learning-based patient selection in an emergency department

arXiv: https://arxiv.org/abs/2206.03752 · HTML: https://mlsanity.com/paper/2206.03752/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 73 references that are in the map.
- First posted on arXiv 8 Jun 2022.

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

- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Reinforcement Learning for Solving the Vehicle Routing Problem](https://mlsanity.com/paper/1802.04240/) (2018)
- [An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem](https://mlsanity.com/paper/1906.01227/) (2019)
- [Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem](https://mlsanity.com/paper/1709.09972/) (2017)
- [Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location](https://mlsanity.com/paper/1907.05765/) (2019)

## Abstract (arXiv)

The performance of Emergency Departments (EDs) is of great importance for any health care system, as they serve as the entry point for many patients. However, among other factors, the variability of patient acuity levels and corresponding treatment requirements of patients visiting EDs imposes significant challenges on decision makers. Balancing waiting times of patients to be first seen by a physician with the overall length of stay over all acuity levels is crucial to maintain an acceptable level of operational performance for all patients. To address those requirements when assigning idle resources to patients, several methods have been proposed in the past, including the Accumulated Priority Queuing (APQ) method. The APQ method linearly assigns priority scores to patients with respect to their time in the system and acuity level. Hence, selection decisions are based on a simple system representation that is used as an input for a selection function. This paper investigates the potential of an Machine Learning (ML) based patient selection method. It assumes that for a large set of training data, including a multitude of different system states, (near) optimal assignments can be computed by a (heuristic) optimizer, with respect to a chosen performance metric, and aims to imitate such optimal behavior when applied to new situations. Thereby, it incorporates a comprehensive state representation of the system and a complex non-linear selection function. The motivation for the proposed approach is that high quality selection decisions may depend on a variety of factors describing the current state of the ED, not limited to waiting times, which can be captured and utilized by the ML model. Results show that the proposed method significantly outperforms the APQ method for a majority of evaluated settings