# Validation of a Hospital Digital Twin with Machine Learning

arXiv: https://arxiv.org/abs/2303.04117 · HTML: https://mlsanity.com/paper/2303.04117/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [Consistent feature attribution for tree ensembles](https://mlsanity.com/paper/1706.06060/) (2017)
- [Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset](https://mlsanity.com/paper/2009.02899/) (2020)

## Abstract (arXiv)

Recently there has been a surge of interest in developing Digital Twins of process flows in healthcare to better understand bottlenecks and areas of improvement. A key challenge is in the validation process. We describe a work in progress for a digital twin using an agent based simulation model for determining bed turnaround time for patients in hospitals. We employ a strategy using machine learning for validating the model and implementing sensitivity analysis.