RT Journal Article
T1 Statistical Validation of Synthetic Data for Lung Cancer Patients Generated by Using Generative Adversarial Networks
A1 Gonzalez-Abril, Luis
A1 Angulo, Cecilio
A1 Antonio Ortega, Juan
A1 Lopez-Guerra, Jose-Luis
K1 personalized medicine
K1 generative adversarial network
K1 lung cancer
K1 validation tools
AB The development of healthcare patient digital twins in combination with machine learning technologies helps doctors in therapeutic prescription and in minimally invasive intervention procedures. The confidentiality of medical records or limited data availability in many health domains are drawbacks that can be overcome with the generation of synthetic data conformed to real data. The use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients has been previously introduced as a tool to solve this problem in the form of anonymized synthetic patients. However, generated synthetic data are mainly validated from the machine learning domain (loss functions) or expert domain (oncologists). In this paper, we propose statistical decision making as a validation tool: Is the model good enough to be used? Does the model pass rigorous hypothesis testing criteria? We show for the case at hand how loss functions and hypothesis validation are not always well aligned.
PB Mdpi
YR 2022
FD 2022-10-01
LK http://hdl.handle.net/10668/20972
UL http://hdl.handle.net/10668/20972
LA en
DS RISalud
RD Apr 19, 2025