Machine Learning in Healthcare

Stavros Pitoglou
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引用次数: 1

Abstract

Machine learning, closely related to artificial intelligence and standing at the intersection of computer science and mathematical statistical theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, machine learning can find a fertile field of application. This chapter's purpose is to give a general non-technical definition of machine learning, provide a review of its latest implementations in the healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.
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医疗保健中的机器学习
机器学习与人工智能密切相关,处于计算机科学和数理统计理论的交叉点,当真相隐藏在人类大脑无法进入的地方时,机器学习就会派上用场。给定任何预测或评估问题,这个问题越复杂,基于人类思维理解内在因果关系/模式并应用常规方法获得可接受的解决方案的难度,机器学习可以找到一个肥沃的应用领域。本章的目的是给出机器学习的一般非技术定义,回顾其在医疗保健领域的最新实现,并增加对该主题的持续讨论。它表明,在已经活跃的学术界之外,实体应该积极参与寻找“利用”现有数据集的解决方案,并将其应用于日常实践,嵌入到已经在使用的软件过程中。
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Machine Learning in Healthcare
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