Publication

Statistical validation of synthetic data for lung cancer patients generated by using generative adversarial networks

Journal Article (2022)

Journal

Electronics

Pages

3277

Volume

11

Number

20

Doc link

https://doi.org/10.3390/electronics11203277

File

Download the digital copy of the doc pdf document

Authors

Abstract

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.

Categories

artificial intelligence.

Author keywords

personalized medicine; generative adversarial network; lung cancer; validation tools

Scientific reference

L. González, C. Angulo, J.A. Ortega and J.L. López. Statistical validation of synthetic data for lung cancer patients generated by using generative adversarial networks. Electronics, 11(20): 3277, 2022.