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Depression prediction based on LassoNet-RNN model: A longitudinal study.

Jiatong Han1, Hao Li1, Han Lin2

  • 1School of Computer Science, Nanjing Audit University, China.

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Summary
This summary is machine-generated.

This study identifies key factors influencing depression using a novel LassoNet-RNN model. Understanding these elements is crucial for developing effective mental health prevention strategies.

Keywords:
CHARLSCharacteristic variablesDepressionLassoNet-RNN

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Area of Science:

  • Public Health
  • Computational Psychiatry
  • Data Science

Background:

  • Depression is a significant global health issue.
  • Identifying depression's root causes is vital for mental health.
  • Preventive strategies require understanding individual influencing factors.

Purpose of the Study:

  • To develop a model for identifying individual depression influencing factors.
  • To analyze multivariate time series data for depression risk.
  • To provide recommendations for public health interventions.

Main Methods:

  • Utilized the China Health and Retirement Longitudinal Study (CHARLS) dataset.
  • Employed a combination of LassoNet and Recurrent Neural Network (RNN) for model construction.
  • Analyzed 27 characteristic variables across demographics, health, economics, and environment.

Main Results:

  • Developed the LassoNet-RNN screening model.
  • Identified 27 significant characteristic variables influencing depression.
  • Ranked the importance of these identified variables.

Conclusions:

  • The LassoNet-RNN model effectively identifies depression influencing factors.
  • Findings offer insights for theoretical advancements in mental health.
  • Results support practical decision-making in public health policy.