Evaluation and Analysis of Elderly Mental Health Based on Artificial Intelligence.

Objective. The purpose is to understand the depression status of the elderly in the community, explore its influencing factors, formulate a comprehensive psychological intervention plan according to the influencing factors, implement demonstration psychological intervention, and evaluate and feedbac...

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Publicado en:Occupational Therapy International pp. 1 - 12
Autor principal: Li, Xiao
Formato: equations & formulas research tables/charts Journal Article
Publicado: Wiley-Blackwell 2/9/2023
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2/9/2023
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2023/7077568
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        atl: Evaluation and Analysis of Elderly Mental Health Based on Artificial Intelligence.
      aug:
        au: Li, Xiao
        affil: Faculty of Humanities and Social Sciences, Beijing University of Technology, Beijing 100124, China
      sug:
        subj:
          Artificial Intelligence
          Depression Diagnosis
          Depression Therapy
          Depression Risk Factors
          Community Living
          Severity of Illness
          Psychotherapy
          Decision Making, Clinical
          Feedback
          Quality of Health Care
          Mental Health Services
          Human
          Aged
          Aged, 80 and Over
          Questionnaires
          Scales
          Self-Management
          Multivariate Analysis
          Logistic Regression
          Algorithms
          Quantitative Studies
          Data Analysis Software
          T-Tests
          Activities of Daily Living
          Male
          Female
          Socioeconomic Factors
          Marital Status
          Funding Source
          Aged: 65+ years
          Aged, 80 & over
          Male
          Female
      ab: Objective. The purpose is to understand the depression status of the elderly in the community, explore its influencing factors, formulate a comprehensive psychological intervention plan according to the influencing factors, implement demonstration psychological intervention, and evaluate and feedback the effect, so as to provide a reference for improving the mental health of the elderly. Method. In order to make the output of different emotional data in LSTM more discriminative, a method to dynamically filter the output of LSTM is proposed. Combining the methods of Attention-LSTM, time-dimensional AI attention, and feature-dimensional AI attention, the best model in this paper is obtained. The multistage stratified cluster sampling method was used to conduct a questionnaire survey on the elderly aged 60 and above in a certain area, including the general demographic characteristics questionnaire of the elderly, the self-rating scale of mental health symptoms, and the health self-management ability of adults. All data were entered into a database using Excel software, and SPSS 19.0 statistical software was used for statistical analysis. Results/Discussion. The detection rate of depression (GDS ≥ 11 points) among the elderly in a community in a certain area was 39.38%. Multivariate logistic regression analysis showed that family history of mental illness, more negative life events, decreased ability of daily living, living alone, and suffering from physical diseases in the past six months were the risk factors for depression in the elderly. Community health education can partially alleviate depression in the elderly. The detection rate and degree of depression of the elderly in the comprehensive psychological intervention group were significantly lower than those in the control group, and the difference was statistically significant (P < 0.05).
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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