TY - CHAP M1 - Book, Section TI - Application of Big Data and Deep Learning in Hematology A1 - Saltz, Joel A2 - Kaushansky, Kenneth A2 - Prchal, Josef T. A2 - Burns, Linda J. A2 - Lichtman, Marshall A. A2 - Levi, Marcel A2 - Linch, David C. PY - 2021 T2 - Williams Hematology, 10e AB - SUMMARYArtificial intelligence (AI) is transforming both biomedical research and the way we practice medicine. It is essential for hematologists to understand the principles underlying AI (or deep learning) methods to understand and evaluate the results produced by software systems and instruments that rely on these methods. We will see that simple mathematical principles, leveraging the huge increase in computational speed available on modern computers, is the underpinning of AI methods.The core technology behind the rapid advances in AI is the neural network. In this chapter, we provide a hematology-focused overview of neural networks. The goal is to impart an understanding of how neural network techniques can be most effectively applied to hematology. This chapter surveys core neural network concepts, presents hematology-related examples, and provides references to the rapidly expanding literature. Many different methodologies can be used in the art and the science of AI. This chapter outlines several of the most important techniques currently in use. As will be described in this chapter, neural networks share many principles in common with commonly used techniques such as linear regression and logistic regression. SN - PB - McGraw-Hill Education CY - New York, NY Y2 - 2024/03/29 UR - accessmedicine.mhmedical.com/content.aspx?aid=1180477033 ER -