research-article

Research on promoting the application of disease prediction system based on machine learnin

Authors:

Shao Rongqiang,

Gong QingyueAuthors Info & Claims

ISAIMS '20: Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences

Pages 45 - 50

https://doi.org/10.1145/3429889.3429898

Published: 04 December 2020 Publication History

Abstract

In order to solve the problem that the disease prediction system based on machine learning has more research and less clinical application, through the analysis of the training and application process of predictive disease model, it points out that the lack of interpretability of disease prediction model and the continuous optimization of disease prediction model are the reasons affecting doctors' use of the model, and proposes that doctors should be involved in the training through business parameters to improve the interpretability of the models and the process of model training and calling should be simplified to improve the experience of the system. Finally, experiments and data analysis prove that the above measures can promote the clinical application of the disease prediction model.

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Introduction of the first author: Shao Rongqiang (1977-), male, senior engineer of the Information Department of Yixing Second People's Hospital, graduate student of College of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, research fields: software development, information research, big data analysis, Artificial Intelligence.

[16]

Chen Yan (1997-), female, postgraduate, School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, research fields: software development, big data analysis, Artificial Intelligence.

[17]

Deng Chang (1979-), male, senior engineer, Information Department of the Fourth People's Hospital of Yixing

[18]

Corresponding author: Gong Qingyue (1972-), associate professor of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, [email protected]

[19]

Contact: Shao Rongqiang, School of Artificial Intelligence and Information Technology, Nanjing University of Traditional Chinese Medicine, 138 Xianlin Avenue, Qixia District, Nanjing, 13584229786, 210046, [email protected]

Cited By

Zhou FHu SDu XWan XLu ZWu J(2023)Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing HomesElectronics10.3390/electronics1204100912:4(1009)Online publication date: 17-Feb-2023
https://doi.org/10.3390/electronics12041009
Turgut YKose YUstundag ACevikcan E(2022)Business Analytics for ManagersBusiness Analytics for Professionals10.1007/978-3-030-93823-9_1(3-20)Online publication date: 10-May-2022
https://doi.org/10.1007/978-3-030-93823-9_1

Index Terms

Research on promoting the application of disease prediction system based on machine learnin
1. Computing methodologies
  1. Machine learning

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ISAIMS '20: Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences

September 2020

313 pages

ISBN:9781450388603

DOI:10.1145/3429889

Copyright © 2020 ACM.

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Publication History

Published: 04 December 2020

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ISAIMS 2020

ISAIMS 2020: 2020 International Symposium on Artificial Intelligence in Medical Sciences

September 11 - 13, 2020

Beijing, China

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ISAIMS '20 Paper Acceptance Rate 53 of 112 submissions, 47%;

Overall Acceptance Rate 53 of 112 submissions, 47%

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Cited By

Zhou FHu SDu XWan XLu ZWu J(2023)Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing HomesElectronics10.3390/electronics1204100912:4(1009)Online publication date: 17-Feb-2023
https://doi.org/10.3390/electronics12041009
Turgut YKose YUstundag ACevikcan E(2022)Business Analytics for ManagersBusiness Analytics for Professionals10.1007/978-3-030-93823-9_1(3-20)Online publication date: 10-May-2022
https://doi.org/10.1007/978-3-030-93823-9_1

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