A framework to create, evaluate and select synthetic datasets for survival prediction in oncology

Data-driven decision-making in radiation oncology (RO) relies on integrating real-world data effectively. Synthetic data (SD), generated through machine learning, offers a solution by mimicking real-world data without compromising privacy. This paper presents a general framework for generating, eval...

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Veröffentlicht in:	Computers in biology and medicine Jg. 192; H. Pt A; S. 110198
Hauptverfasser:	Christoforou, A.T., Spohn, S.K.B., Sprave, T., Fechter, T., Rühle, A., Nicolay, N.H., Popp, I., Grosu, A.L., Peeken, J.C., Thieme, A.H., Stylianopoulos, T., Strouthos, I., Ferentinos, K., Roussakis, Y., Zamboglou, C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.06.2025
Schlagworte:	Artificial Intelligence Databases, Factual Deep learning Evaluation Female Framework Humans Internal Medicine Machine Learning Male Medicine Neoplasms - mortality Oncology Other Prostatic Neoplasms - mortality Retrospective Studies Survival Analysis Synthetic Data Deep learning Medicine Evaluation Artificial Intelligence Survival Analysis Oncology Synthetic Data Framework
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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