Research progress on machine learning XGBoost algorithm in medicine

QI Qiaona; LIU Yan; CHEN Jihui; LIU Xinzhu; YANG Rui; ZHANG Jinyuan; CUI Mengxuan; XIE Yimeng; WANG Zeyuan; YU Ze; GAO Fei; ZHANG Jian

doi:10.12122/j.issn.1674-4500.2021.05.25

Volume 44 Issue 5

Sep. 2021

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Article Navigation > Journal of Molecular Imaging > 2021 > 44(5): 856-862

QI Qiaona, LIU Yan, CHEN Jihui, LIU Xinzhu, YANG Rui, ZHANG Jinyuan, CUI Mengxuan, XIE Yimeng, WANG Zeyuan, YU Ze, GAO Fei, ZHANG Jian. Research progress on machine learning XGBoost algorithm in medicine[J]. Journal of Molecular Imaging, 2021, 44(5): 856-862. doi: 10.12122/j.issn.1674-4500.2021.05.25

Citation:

QI Qiaona, LIU Yan, CHEN Jihui, LIU Xinzhu, YANG Rui, ZHANG Jinyuan, CUI Mengxuan, XIE Yimeng, WANG Zeyuan, YU Ze, GAO Fei, ZHANG Jian. Research progress on machine learning XGBoost algorithm in medicine[J]. Journal of Molecular Imaging, 2021, 44(5): 856-862. doi: 10.12122/j.issn.1674-4500.2021.05.25

Citation:

QI Qiaona, LIU Yan, CHEN Jihui, LIU Xinzhu, YANG Rui, ZHANG Jinyuan, CUI Mengxuan, XIE Yimeng, WANG Zeyuan, YU Ze, GAO Fei, ZHANG Jian. Research progress on machine learning XGBoost algorithm in medicine[J]. Journal of Molecular Imaging, 2021, 44(5): 856-862. doi: 10.12122/j.issn.1674-4500.2021.05.25

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Research progress on machine learning XGBoost algorithm in medicine

doi: 10.12122/j.issn.1674-4500.2021.05.25

1.
Department of Medicine, Beijing Medicinovo Technology Co., Ltd., Beijing 100161, China
2.
Department of Clinical Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200092, China

Received Date: 2021-08-05
Publish Date: 2021-09-20

Abstract

Abstract

The XGBoost algorithm was first proposed in 2014. Based on the boosting algorithm, it has shown its excellent performance and usability in many data science competitions. At present, the classification and regression models for prediction based on XGBoost algorithm have been widely used for data analysis in health care, finance, education, manufacturing, and other fields. In the medical field, XGBoost has been used for disease diagnosis, prediction of risk, outcomes, prognosis, rational and safe drug use, drug research and development, etc. It provides solutions with great possibilities to improve the efficiency and quality of decision making and reduce the false positive rate. At the same time, XGBoost can automatically learn the splitting direction when processing missing values. It can also simulate nonlinear effect when dealing with large data sets with a high efficiency and accuracy.
- XGBoost,
- machine learning,
- medical big data,
- clinical decision support,
- classification and regression model for prediction

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References(49)

References

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