IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i6p3599-d773921.html
   My bibliography  Save this article

Design and Preliminary Realization of a Screening and Early Warning Health Management System for Populations at High Risk for Depression

Author

Listed:
  • Xin Chen

    (School of Public Health, Hangzhou Normal University, Hangzhou 311121, China
    Engineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou Normal University, Hangzhou 311121, China
    Institute of VR and Intelligent System, Hangzhou Normal University, Hangzhou 311121, China)

  • Liangwen Xu

    (School of Public Health, Hangzhou Normal University, Hangzhou 311121, China
    Engineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou Normal University, Hangzhou 311121, China)

  • Zhigeng Pan

    (Institute of VR and Intelligent System, Hangzhou Normal University, Hangzhou 311121, China
    School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

Depression has a high incidence in the world. Based on the concept of preventive treatment of disease of traditional Chinese medicine, timely screening and early warning of depression in populations at high risk for this condition can avoid, to a certain extent, the dysfunctions caused by depression. This work studied a method to collect information on depression, generate a database of depression features, design algorithms for screening populations at high risk for depression and creating an early warning model, develop an early warning short-message service (SMS) platform, and implement a scheme of depression screening and an early warning health management system. The implementation scheme included mobile application (app), cloud form, screening and early warning model, cloud platform, and computer software. Multiple modules jointly realized the screening, early warning, and management of the health functions of individuals at high risk for depression. At the same time, function modules such as mobile app and cloud form for collecting depression health information, early warning SMS platform, and health management software were designed, and the functions of the modules were preliminarily developed. Finally, the black-box test and white-box test were used to assess the system’s functions and ensure the reliability of the system. Through the integration of mobile app and computer software, this study preliminarily realized the screening and early warning health management of a population at high risk for depression.

Suggested Citation

  • Xin Chen & Liangwen Xu & Zhigeng Pan, 2022. "Design and Preliminary Realization of a Screening and Early Warning Health Management System for Populations at High Risk for Depression," IJERPH, MDPI, vol. 19(6), pages 1-12, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3599-:d:773921
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/6/3599/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/6/3599/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xin Chen & Zhigeng Pan, 2021. "A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health," IJERPH, MDPI, vol. 18(12), pages 1-12, June.
    2. Abolfazl Mollalo & Kiara M. Rivera & Behzad Vahedi, 2020. "Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Munazza Fatima & Kara J. O’Keefe & Wenjia Wei & Sana Arshad & Oliver Gruebner, 2021. "Geospatial Analysis of COVID-19: A Scoping Review," IJERPH, MDPI, vol. 18(5), pages 1-14, February.
    2. Abiodun O. Oluyomi & Sarah M. Gunter & Lauren M. Leining & Kristy O. Murray & Chris Amos, 2021. "COVID-19 Community Incidence and Associated Neighborhood-Level Characteristics in Houston, Texas, USA," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    3. Abolfazl Mollalo & Moosa Tatar, 2021. "Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
    4. Anil Babu Payedimarri & Diego Concina & Luigi Portinale & Massimo Canonico & Deborah Seys & Kris Vanhaecht & Massimiliano Panella, 2021. "Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-11, April.
    5. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    6. Sandi Baressi Šegota & Ivan Lorencin & Nikola Anđelić & Jelena Musulin & Daniel Štifanić & Matko Glučina & Saša Vlahinić & Zlatan Car, 2022. "Applying Regressive Machine Learning Techniques in Determination of COVID-19 Vaccinated Patients’ Influence on the Number of Confirmed and Deceased Patients," Mathematics, MDPI, vol. 10(16), pages 1-24, August.
    7. Abdallah S. A. Yaseen, 2022. "Impact of social determinants on COVID-19 infections: a comprehensive study from Saudi Arabia governorates," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    8. Xin Jing & Jin Seo Cho, 2023. "Forecasting the Confirmed COVID-19 Cases Using Modal Regression," Working papers 2023rwp-217, Yonsei University, Yonsei Economics Research Institute.
    9. Sungchul Mun & Sangin Park & Sungyop Whang & Mincheol Whang, 2022. "Effects of Temporary Respiration Exercise with Individual Harmonic Frequency on Blood Pressure and Autonomic Balance," IJERPH, MDPI, vol. 19(23), pages 1-19, November.
    10. Lorenzo Gianquintieri & Maria Antonia Brovelli & Andrea Pagliosa & Gabriele Dassi & Piero Maria Brambilla & Rodolfo Bonora & Giuseppe Maria Sechi & Enrico Gianluca Caiani, 2022. "Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning," IJERPH, MDPI, vol. 19(15), pages 1-19, July.
    11. Seung-Hun Lee & Hyeon-Seong Ju & Sang-Hun Lee & Sung-Woo Kim & Hun-Young Park & Seung-Wan Kang & Young-Eun Song & Kiwon Lim & Hoeryong Jung, 2021. "Estimation of Health-Related Physical Fitness (HRPF) Levels of the General Public Using Artificial Neural Network with the National Fitness Award (NFA) Datasets," IJERPH, MDPI, vol. 18(19), pages 1-13, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3599-:d:773921. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.