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Study on early warning treatment of senile depression in community based on artificial intelligence model

Published online by Cambridge University Press:  09 March 2023

Jiali Xiao ,
Yifeng Li ,
Lihua Li  and
Yan Tian
Jiali Xiao
Affiliation:
Beijing Institute of Technology Zhuhai, Zhuhai 519000, China Universiti Sains Malaysia, Penang 11800, Malaysia
Yifeng Li
Affiliation:
Beijing Institute of Technology Zhuhai, Zhuhai 519000, China
Lihua Li
Affiliation:
Beijing Institute of Technology Zhuhai, Zhuhai 519000, China
Yan Tian*
Affiliation:
Beijing Institute of Technology Zhuhai, Zhuhai 519000, China
*
*Corresponding author.
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Abstract

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Background

In recent years, with the acceleration of population aging in China, the number of elderly people with depression is increasing. Artificial intelligence models and data analysis have sound applications in the early warning and treatment of the elderly with depression in the community by finding the elderly with depression timely and carrying out early warning treatment for them.

Subjects and Methods

50 elderly people with depression, from two communities of equal size, were randomly selected to participate in the experiment. Among them, one community adopts routine management and treatment, and the other community conducts early-warning treatment based on an artificial intelligence model and data analysis. The former and the latter were used as the observation group and the intelligent group respectively. All the elderly were evaluated according to the Geriatric Depression Scale (GDS) before and 6 months after the experiment.

Results

The GDS scores of the elderly in the observation group and the intelligent group before and after the experiment are shown in Table 1. The GDS scores of the observation group and the intelligent group are close before and after the experiment from Table 1. Six months after the experiment, the GDS scores of the intelligent group are significantly lower than that of the observation group. In this experiment, P < 0.0 indicates that the difference is statistically significant.Table 1.

GDS score results of the two groups of elderly before and after the experiment

GroupProjectGDS score before the experimentGDS score after theexperiment
Observation groupScore17.18±1.4514.71±1.13
t0.1251.234
P0.0340.039
Intelligent groupScore16.31±1.2710..28±0.97
t0.2431.314
P0.0190.028

Conclusions

According to statistics, the incidence rate of depression in the elderly can reach 10%, so it is necessary to strengthen the early warning and treatment of depression symptoms in the elderly. The artificial intelligence model and data analysis can help find the depressive symptoms of the elderly in the community as early as possible, and help take measures to carry out early warning treatment, thereby improving the depressive situation.

Acknowledgement

The research is supported by: Provincial key platforms and major scientific research projects of Guangdong universities “Building an intelligent community public welfare platform based on blockchain (No. 2021ZDZX3004)”.

Type
Abstracts
Information
CNS Spectrums , Volume 28 , Supplement S1: Abstracts of the International Conference on Psychiatry and Neurorestoratology (ICPN2022) December 24, 2022 Zhengzhou, China , March 2023 , pp. S34
Copyright
© The Author(s), 2023. Published by Cambridge University Press