skip to main content
10.1145/3429889.3429898acmotherconferencesArticle/Chapter ViewAbstractPublication PagesisaimsConference Proceedingsconference-collections
research-article

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

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.

References

[1]
Zou QC, Zhang T, Wu RP, Ma JX, Li MC, Chen C, Hou CY. (2018) Automation to Intelligence: Survey of research on vulnerability discovery techniques. Journal of Tsinghua University (Science and Technology), 58 (12): 45--50
[2]
Meng Q, Shameng W, Chao F, et al. (2016) Predicting buffer overflow using semi-supervised learning[C]// 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2016.
[3]
Dam HK, Tran T, Pham T, Ng SW, Grundy J, Ghose A. (2017) Automatic Feature Learning for Vulnerability Prediction. ArXiv Preprint arXiv:1708.02368.
[4]
Yamaguchi F, Maier a. Gascon H, Rieck k. (2015) Automatic inference of search patterns for taint - style vulnerabilities. In: Proc. Of the IEEE Symp. On Security & Privacy. 2015. ( / SP., 2015.54)
[5]
Zhen L, Zou D, Xu S, Ou X, Hai J, Wang S, Deng Z, Zhong Y. VulDeePecker: (2018) A deep learn-based system for Vulnerability Detection. In: Proc. Of the 25 th Annual Network and Distributed System Security Symp. (NDSS). 2018. [
[6]
Lin G, Zhang J, Luo W, Pan L, Xiang Y, Vel OD, Montague P. (2018) Cross-project Transfer Representation Learning for vulnerable function Discovery. IEEE Trans. On Industrial Informatics, 2018, 14 (7): 1. [ / TII. 2018.2821768]
[7]
Mou L, Li G, Jin Z, et al. (2016) Convolutional Neural Network over Tree Structures for Programming Language Processing[C]// Thirtieth Aaai Conference on Artificial Intelligence. AAAI Press, 2016.
[8]
Perl H, S, M, Smith Arp D, Yamaguchi, F Rieck K, Fahl S, Acar y. (2015) VCCFinder: Finding potential vulnerabilities in opensource projects to assist code audits. In: Proc. Of the ACM SIGSAC Conf. On Computer & Communications Security. 2015. [
[9]
Younis A, Malaiya YK, Anderson C, Ray I.(2016) To fear or not To fear that is the question: Code characteristics of A vulnerable function with an existing exploit. In: Proc. of the conf. On Data & Applications Security & Privacy. 2016. [
[10]
Meng Q, Zhang B, Feng C, Tang C. (2016) Detecting buffer Boundary violations Based on SVM. Proc. Of the 3 rd Intel Conf. On Information Science and Control Engineering (ICISCE). IEEE, 2016. 313--316. 2016.76] [ / ICISCE.
[11]
Deo RC. (2015) Machine learning in medicine [J]. Circulation, 2015, 132: 1920 1930.
[12]
Erickson BJ, Korfiatis P, Akkus Z, Kline TL.(2017) Machine Learning for Medical Imaging [J]. Radiographics, 2017, 37:50 5--15.
[13]
Eric J, Topol. (2019) High-performance medicine: the convergence of human and artificial intelligence.[J]. Nature medicine, 2019, 25:44.
[14]
Kawamoto K, Houlihan CA, Balas EA, et al. (2005) Improving Clinical Practice using Clinical Decision Support Systems: A systematic review of trials to identify features critical to success. BMJ, 2005, 330(7494): 765.
[15]
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

View all
  • (2023)Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing HomesElectronics10.3390/electronics1204100912:4(1009)Online publication date: 17-Feb-2023
  • (2022)Business Analytics for ManagersBusiness Analytics for Professionals10.1007/978-3-030-93823-9_1(3-20)Online publication date: 10-May-2022

Index Terms

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

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ISAIMS '20: Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences
    September 2020
    313 pages
    ISBN:9781450388603
    DOI:10.1145/3429889
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 December 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Business parameters
    2. Machine learning
    3. disease prediction
    4. process Optimization

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ISAIMS 2020

    Acceptance Rates

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

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)3
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 25 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing HomesElectronics10.3390/electronics1204100912:4(1009)Online publication date: 17-Feb-2023
    • (2022)Business Analytics for ManagersBusiness Analytics for Professionals10.1007/978-3-030-93823-9_1(3-20)Online publication date: 10-May-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media