Applications of machine learning in clinical decision support in the omic era
Received date: 2018-05-17
Revised date: 2018-07-23
Online published: 2018-07-30
Supported by
Supported by the National Key Research and Development Program of China(2016YFC0901700);Supported by the National Key Research and Development Program of China(2016YFC0901603);Supported by the National Key Research and Development Program of China(2017YFC0907502);Supported by the National Key Research and Development Program of China(2017YFC0908402);Supported by the National Key Research and Development Program of China(2017YFC0907405)
With the development of the omic technologies, the acquisition approaches of various biological data on different levels and types are becoming more mature. As a large amount of data will be produced in the process of diagnosis and treatment of diseases, it is necessary to utilize the artificial intelligence such as machine learning to analyze complex, multi-dimensional and multi-scale data and to construct clinical decision support tools. It will provide a method to figure out rapid and effective programs in diagnosis and treatment. In this process, the choice of artificial intelligence seems to be particularly important, such as machine learning. The article reviews the type and algorithm of machine learning used in clinical decision support, such as support vector machines, logistic regression, clustering algorithms, Bagging, random forests and deep learning. The application of machine learning and other methods in clinical decision support has been summarized and classified. The advantages and disadvantages of machine learning are elaborated. It will provide a reference for the selection between machine learning and other artificial intelligence methods in clinical decision support.
Zhao Xuetong, Yang Yadong, Qu Hongzhu, Fang Xiangdong . Applications of machine learning in clinical decision support in the omic era[J]. Hereditas(Beijing), 2018 , 40(9) : 693 -703 . DOI: 10.16288/j.yczz.18-139
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