Generative adversarial networks for anonymized healthcare of lung cancer patients
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 pat...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/354398 |
| Acceso en línea: | https://hdl.handle.net/2117/354398 https://dx.doi.org/10.3390/electronics10182220 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Lungs--Cancer Artificial intelligence Digital twin Anonymization Generative adversarial network Lung cancer Xarxes neuronals (Informàtica) -- Aplicacions Aprenentatge automàtic Pulmons -- Càncer Intel·ligència artificial Àrees temàtiques de la UPC::Informàtica |
| Sumario: | 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. |
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