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Schizophrenia Auxiliary Diagnosis System Based on Data Mining Technology

  • Xiaohong Wang
  • Na Zhao
  • Peng Ouyang
  • Jiayi Lin
  • Jian HuEmail author
Image & Signal Processing
  • 37 Downloads
Part of the following topical collections:
  1. Artificial Intelligence Application in Health Informatics

Abstract

In order to use digital medical technology to develop and design an auxiliary diagnosis system for schizophrenia to assist doctors at all levels to diagnose and predict the cure of patients, improve the accuracy of diagnosis of symptoms, find complications in advance, and reduce the risk of disease, the application of Bayesian network in auxiliary diagnosis system of schizophrenia is studied, and an auxiliary diagnosis system of schizophrenia is designed. Based on data mining technology, knowledge information can be found from patient data and used to diagnose the nature of patients. The demand analysis of auxiliary diagnosis system is briefly introduced, and an auxiliary diagnosis system for schizophrenia based on Bayesian network is designed.

Keywords

Bayesian network Auxiliary diagnosis system Demand analysis Functional design Data mining 

Notes

Funding

There was no dedicated funding regarding this study.

Compliance with Ethical Standards

Conflict of Interest

Author Xiaohong Wang declares that he has no conflict of interest. Author Na Zhao declares that he has no conflict of interest. Author Peng Ouyang declares that he has no conflict of interest. Author Jiayi Lin declares that he has no conflict of interest. Author Jian Hu declares that he has no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

This article does not contain any studies with animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Xiaohong Wang
    • 1
  • Na Zhao
    • 1
  • Peng Ouyang
    • 2
  • Jiayi Lin
    • 3
  • Jian Hu
    • 1
    Email author
  1. 1.Department of PsychiatryThe First Affiliated Hospital of Harbin Medical UniversityHarbinChina
  2. 2.School of ManagementHarbin Institute of TechnologyHarbinChina
  3. 3.Beijing Electro-Mechanical Engineering InstituteBeijingChina

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