Generative Adversarial Networks for Anonymized Healthcare of Lung Cancer Patients

Abstract

The digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination with machine learning technologies, it should help doctors in therapeutic path and in minimally invasive intervention procedures. Confidentiality of medical records is a very delicate issue, therefore some anonymization process is mandatory in order to maintain patients privacy. Moreover, data availability is very limited in some health domains like lung cancer treatment. Hence, generation of synthetic data conformed to real data would solve this issue. In this paper, the use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients is introduced as a tool to solve this problem in the form of anonymized synthetic patients. Generated synthetic patients are validated using both statistical methods, as well as by oncologists using the indirect mortality rate obtained for patients in different stages.

Patient digital twins: an introduction based on a scoping review

“…The first advantage of this definition is that it can exclude what is not a patient digital twin: generic models of cells, tissues, organs, or biological systems not linked to a patient but used to study disease progression or drug development 29,54,80 ; pure cyber-physical systems, that is, systems such as implantable cardioverter-defibrillators, which do not use a representation of the patient and therefore not a "viewable" digital replica of the patient; digital patient data created from patient databases for in silico trials; 47,99,103 often using generative adversarial networks, which we propose to call "synthetic patients" instead; data sets from another patient, similar to those of the index patient; 77 machine learning based classifiers, trained on a population to predict a diagnosis; 69 and patient models built from a single data source, such as demographic characteristics or imaging. 31,47,49 The second advantage of this definition is that it encompasses the two major trends in patient digital twins revealed by this study. On one hand, digital twins offering a high degree of fidelity, combining advanced anatomical and physiological models, based on mechanistic approaches or combining mechanistic and data-driven approaches.…”

A Case for Synthetic Data in Regulatory Decision‐Making in Europe

“…Currently, the need to optimize data sharing and utility has led to the development of innovative privacy enhancing technologies (i.e., homomorphic or blockchain encryption, generative adversarial networks (GANs), and federated learning [11][12][13] ) that could help to overcome inherent limitations of RWD. Different techniques for synthetic data generation and criteria continue to evolve.…”

Optimizing Clinical Diabetes Diagnosis through Generative Adversarial Networks: Evaluation and Validation

“…Gonzalez-Abril et al [ 7 ] embarked on generating synthetic lung cancer patient data, providing critical insights into the potential applications of synthetic datasets for medical research. Their work underscores the utility of GANs in generating patient profiles, yet it primarily focused on a specific medical condition.…”