Prediction, Diagnosis and Treatment of Depression in Recent Ten Years

Chenrui Bai

doi:10.54254/2753-8818/2025.20448

Research Article

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Published on 15 January 2025

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Bai,C. (2025). Prediction, Diagnosis and Treatment of Depression in Recent Ten Years. Theoretical and Natural Science,89,64-70.

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Prediction, Diagnosis and Treatment of Depression in Recent Ten Years

Chenrui Bai *^,1,

¹ University of Shanghai for Science and Technology, Shanghai City, 200093, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/2025.20448

Abstract

Depression is the second leading cause of disability in the world and has become one of the most prevalent mental illnesses in the world. Due to the high recurrence rate of depression and the fact that it is a disease that can be accompanied by the development of suicidal and bipolar tendencies, it is a great challenge to society and the financial and energy resources of hospital patients' families. However, there is no complete diagnosis and treatment system for depression, so this paper is committed to comprehensively review the contributions of the current studies of the depression diagnosis and treatment in the field of neural activity analysis, machine learning detection technology and neuromodulation, and discusses the development of prediction-diagnosis-treatment path in the future.

Keywords

Depression, Machine learning, Neuro modulation, Functional connectivity

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References

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Cite this article

Bai,C. (2025). Prediction, Diagnosis and Treatment of Depression in Recent Ten Years. Theoretical and Natural Science,89,64-70.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Modern Medicine and Global Health

Conference website: https://2025.icmmgh.org/

ISBN：978-1-83558-929-8(Print) / 978-1-83558-930-4(Online)

Conference date: 10 January 2025

Editor：Sheiladevi Sukumaran

Series: Theoretical and Natural Science

Volume number: Vol.89

ISSN：2753-8818(Print) / 2753-8826(Online)

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