Augmenting ESM-based Mental Health Assessment using Affective Ising Model

DOI

10.2991/978-94-6463-388-7_17How to use a DOI?

Keywords

Mental health; Assessment; Experience sampling method; Affective Ising model; Energy landscape; Emotional experience; Model-based analysis

Abstract

This study presents a novel approach to augment the accuracy and granularity of mental health assessment using a combination of Experience Sampling Methodology (ESM) and the Affective Ising Model. Traditional methods often lack the ability to capture the dynamic and nuanced nature of an individual's mental health. We propose a data-driven framework that leverages ESM data to construct an individual’s positive and negative affect space, thus enabling a comprehensive analysis of their emotion landscape. To achieve this, we utilize the Affective Ising Model, a statistical physics-based framework that extracts the energy landscape of an individual’s affect space, providing insights into their underlying mental state dynamics. We illustrate our approach using synthetic data generated from existing ESM datasets, ensuring a controlled yet realistic representation of affective states. Parameter estimates of the Affective Ising Model were shown to categorize different potential mental health states. This characterization aids in identifying potential markers or indicators of specific mental health challenges, thus facilitating early diagnosis and possible personalized interventions. The proposed method is hoped to provide a robust framework for augmenting mental health assessment, offering a more comprehensive understanding of an individual’s emotional experiences and their potential mental health states.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Gina Rose N. Tongco-Rosario
AU  - Jaymar Soriano
PY  - 2024
DA  - 2024/02/29
TI  - Augmenting ESM-based Mental Health Assessment using Affective Ising Model
BT  - Proceedings of the Workshop on Computation: Theory and Practice (WCTP 2023)
PB  - Atlantis Press
SP  - 269
EP  - 276
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-388-7_17
DO  - 10.2991/978-94-6463-388-7_17
ID  - Tongco-Rosario2024
ER  -