[1K3-GS-10] AI application: Medicine / Healthcare

[1K3-GS-10-01] Quantitative understanding of depression states in COVID-19 pandemic using energy landscape analysis (ELA)

〇Daiki Tatematsu¹, Naotoshi Nakamura¹, Shinsuke Koike², Shingo Iwami¹ (1. Nagoya University, 2. Tokyo University)

Keywords:Energy landscape Analysis(ELA), Psychiatry, COVID-19

The COVID-19 pandemic changed our lifestyles. It is expected that the changes in depression state also occurred because of these changes. In this study, we used the questionnaire responses that asked high school students in Tokyo about their depression states before, during, and after the period of the COVID-19 pandemic and analyzed the group characteristics of changes in depression state as a landscape using energy landscape analysis (ELA), a method of multidimensional (time-series) data analysis. As a result, we were able to quantitatively confirm the depression state changes as the energy barriers. We were also able to detect how the energy barrier changed in the COVID-19 pandemic and found that the energy barrier was relatively high in the COVID-19 pandemic. These results are consistent with those of previous studies and suggest that ELA can be used for the psychiatric questionnaires analysis.