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Individualizing deep dynamic models for psychological resilience data.

Göran Köber1,2, Shakoor Pooseh3,4, Haakon Engen5,6

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This study introduces a dynamic deep learning method for modeling individual psychological resilience using ordinary differential equations (ODEs). The approach effectively analyzes complex longitudinal data, even with incomplete information, to identify resilience factors.

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Area of Science:

  • Computational neuroscience
  • Psychological modeling
  • Machine learning

Background:

  • Deep learning, particularly variational autoencoders, excels at identifying complex data patterns by mapping data to a latent space.
  • Psychological resilience research often involves longitudinal data with intermittent measurements of stressors and mental health, posing analytical challenges.
  • Existing methods struggle with individualized, dynamic modeling of such complex, incomplete datasets.

Purpose of the Study:

  • To propose a novel deep learning approach for individualized, dynamic modeling of psychological resilience.
  • To develop a technique for estimating person-specific ordinary differential equations (ODEs) parameters from sparse, incomplete longitudinal data.
  • To link baseline characteristics to individual responses for identifying resilience factors.

Main Methods:

  • Utilized variational autoencoders for non-linear data mapping into a low-dimensional latent space.
  • Developed a novel technique to estimate person-specific ODE parameters from intermittent, incomplete longitudinal data with varying observations per individual.
  • Employed regularized regression to connect baseline characteristics with individual dynamic responses.

Main Results:

  • Successfully modeled individualized, dynamic responses to stressors within the latent space.
  • Demonstrated the ability to identify person-specific ODE parameters even with limited data.
  • The approach successfully linked baseline characteristics to individual resilience responses in an exemplary application.

Conclusions:

  • The proposed dynamic deep learning method enables individualized, dynamic modeling of complex longitudinal data.
  • The novel ODE parameter estimation technique is effective for sparse and incomplete psychological data.
  • This approach offers a powerful tool for understanding and identifying factors contributing to psychological resilience and has broader applicability.