APPLICATION OF MACHINE LEARNING IN HEALTHCARE
A quick rundown of machine learning-based methodologies and learning algorithms, such as supervised, unsupervised, and reinforcement learning, in many healthcare domains, such as genetics, neuroimaging, radiology, and electronic health records.
Abstract
Signicant progress has been made in the areas of disease populations, disease status, immunological response, and health emergency prediction and identication, among others, thanks to recent developments in AI and MLtechnologies. The use of ML-based approaches in healthcare settings is growing quickly, despite ongoing skepticism about the usefulness of these approaches and how to interpret their ndings. Here, using examples, we give a quick rundown of machine learning-based methodologies and learning algorithms, such as supervised, unsupervised, and reinforcement learning. Second, we go over the use of machine learning (ML) in many healthcare domains, such as genetics, neuroimaging, radiology, and electronic health records. We also offer recommendations for future applications and a brief discussion of the risks and difculties associated with using machine learning to healthcare, including issues with system privacy and ethics.