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An introduction to machine learning for clinicians: How can machine learning augment knowledge in geriatric oncology?

2021; Elsevier BV; Volume: 12; Issue: 8 Linguagem: Inglês

10.1016/j.jgo.2021.03.012

1879-4076

Erika Ramsdale, Eric M. Snyder, Eva Culakova, Huiwen Xu, Adam C. Dziorny, Shuhan Yang, Martin S. Zand, Ajay Anand,

Radiomics and Machine Learning in Medical Imaging

Interest in machine learning (ML) approaches to analyze patient data is in an explosive phase of growth. Underpinning this burgeoning interest are several advances in technology, including availability of computing power, evolution of ML software, and the ubiquity of electronic health records (EHRs). Personal computers now have sufficient computational power to run some ML algorithms for small to medium datasets, encompassing many of the datasets of interest in clinical medicine. Even for very large datasets ("big data", with number of data points in the billions, trillions, or more), access to parallel computing resources is now widespread at academic medical centers and via cloud computing platforms.