Validation of a Hospital Digital Twin with Machine Learning

03/07/2023
by   Muhammad Aurangzeb Ahmad, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/30/2021

A Literature Review on Length of Stay Prediction for Stroke Patients using Machine Learning and Statistical Approaches

Hospital length of stay (LOS) is one of the most essential healthcare me...
research
05/05/2020

Using Machine Learning to Emulate Agent-Based Simulations

In this paper, we evaluate the performance of multiple machine-learning ...
research
05/17/2023

Nine tips for ecologists using machine learning

Due to their high predictive performance and flexibility, machine learni...
research
06/02/2020

Good pivots for small sparse matrices

For sparse matrices up to size 8 × 8, we determine optimal choices for p...
research
12/03/2021

Hybrid Digital Twin for process industry using Apros simulation environment

Making an updated and as-built model plays an important role in the life...
research
06/15/2021

Achieving digital-driven patient agility in the era of big data

There is still a limited understanding of the necessary skill, talent, a...
research
05/09/2022

Methodology to Create Analysis-Naive Holdout Records as well as Train and Test Records for Machine Learning Analyses in Healthcare

It is common for researchers to holdout data from a study pool to be use...

Please sign up or login with your details

Forgot password? Click here to reset