The application of meta-learning in the field of disease prediction and detection

作者：Yujing Xia, Xiaoai Gu, Lin Liu, Lin Tang · 发表于：2021 IEEE International Conference on Electronic Technology, Communication and Information (ICETCI) · 年份：2021 · DOI：10.1109/icetci53161.2021.9563551 · 被引用次数：2 · 研究领域：Machine Learning in Healthcare、Artificial Intelligence in Healthcare、Traditional Chinese Medicine Studies

In the field of disease prediction and detection, due to the lack of medical data, the amount of data stored in the public data set is small and the data is very complex. It is difficult to collect a large number of fully labeled medical data, and there is currently no based meta-learning Articles on disease prediction and detection, Therefore, in response to this problem, this article summarizes the difference between meta-learning and traditional machine learning, the main processes and methods of disease prediction and detection based on meta-learning methods, and the analysis of the mainstream meta-learning framework MAML, as well as the existing meta-learning framework based on MAML. Comparison of learning research methods; as far as current research is concerned, there are few related articles in the field of disease prediction based on machine learning. I hope that the methods and comparisons summarized in this article can make relevant researchers familiar with the progress and technology of existing research.