医疗保健行业的机器学习解决方案:综述

Devi P. Bharathi, P. Ravindra, Kumar R. Kiran
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引用次数: 0

摘要

机器学习(ML)已成为医疗保健行业日益流行的工具,为从诊断和治疗到药物发现和人口健康管理的广泛应用提供解决方案。本文总结了机器学习在医疗保健领域的现状,并强调了该领域的主要趋势和挑战。涵盖的主题包括用于医学成像的深度学习算法、用于个性化治疗计划的强化学习,以及用于识别大型医疗数据集中模式的无监督学习。本文还讨论了在医疗保健中使用机器学习的伦理和隐私影响,以及对机器学习模型进行稳健评估和验证的需求。总体而言,本文展示了机器学习在彻底改变医疗保健方面的潜力,同时也强调了该领域进一步研究和开发的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning solutions for the healthcare industry: A review
Machine Learning (ML) has become an increasingly popular tool in the healthcare industry, providing solutions for a wide range of applications, from diagnosis and treatment to drug discovery and population health management. This paper summarizes the current state of Machine Learning in healthcare and highlights key trends and challenges in the field. Topics covered include deep learning algorithms for medical imaging, reinforcement learning for personalized treatment plans, and unsupervised learning for identifying patterns in large healthcare data sets. This paper also discusses the ethical and privacy implications of using Machine Learning in healthcare and the need for robust evaluation and validation of Machine Learning models. Overall, this paper demonstrates the potential of Machine Learning to revolutionize healthcare while also highlighting the need for further research and development in the field.
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