10.4121/21407604.v2
Ginger Egberts
Ginger
Egberts
0000-0003-3601-6496
Fred Vermolen
Fred
Vermolen
0000-0003-2212-1711
Paul van Zuijlen
Paul van
Zuijlen
0000-0003-3461-8848
Code supporting the paper: A Bayesian finite-element trained machine learning approach for predicting post-burn contraction
4TU.ResearchData
2023
Software
Applied Mathematics
Machine learning
Post-burn scar contraction
Morphoelasticity
Feed-forward neural network
Medical application
Monte Carlo simulations
Matlab
Python
TU Delft, Delft Institute of Applied Mathematics
University of Hasselt, Department of Mathematics and Statistics
Burn Centre and Department of Plastic, Reconstructive and Hand Surgery, Red Cross Hospital, Beverwijk, Netherlands
Department of Plastic, Reconstructive and Hand Surgery, Amsterdam UMC
Pediatric Surgical Centre, Emma Children's Hospital, Amsterdam UMC
2023-01-31
10.1007/s00521-021-06772-3
*.zip; *.m; *.mat; *.py; *.txt; *.png
2
CC BY-NC 4.0
<p>This online resource shows two archived folders: <em>Matlab</em> and <em>Python</em>, that contain relevant code for the article: <em>A Bayesian finite-element trained machine learning approach for predicting post-burn contraction</em>. </p>
<p><br></p>
<p>One finds the codes used to generate the large dataset within the <em>Matlab</em> folder. Here, the file <em>Main.m</em> is the main file and from there, one can run the Monte Carlo simulation. There is a README file.</p>
<p><br></p>
<p>Within the <em>Python</em> folder, one finds the codes used for training the neural networks and creating the online application. The file <em>Data.mat</em> contains the data generated by the Matlab Monte Carlo simulation. The files <em>run_bound.py, run_rsa.py</em>, and <em>run_tse.py</em> train the neural networks, of which the best scoring ones are saved in the folder <em>Training</em>. The<em> DashApp</em> folder contains the code for the creation of the Application.</p>
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Dutch Burn Foundation, project 17.105