A novel approach to anxiety level prediction using small sets of judgment and survey variables

Sumra Bari; Byoung-Woo Kim; Nicole L Vike; Shamal Lalvani; Leandros Stefanopoulos; Nicos Maglaveras; Martin Block; Jeffrey Strawn; Aggelos K Katsaggelos; Hans C Breiter

doi:10.1038/s44184-024-00074-x

A novel approach to anxiety level prediction using small sets of judgment and survey variables

Npj Ment Health Res. 2024 Jun 18;3(1):29. doi: 10.1038/s44184-024-00074-x.

Authors

Sumra Bari¹, Byoung-Woo Kim^#¹, Nicole L Vike^#¹, Shamal Lalvani^#², Leandros Stefanopoulos^#^{2

3}, Nicos Maglaveras³, Martin Block⁴, Jeffrey Strawn⁵, Aggelos K Katsaggelos^{2

6

7}, Hans C Breiter^{8

9

10}

Affiliations

¹ Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA.
² Department of Electrical Engineering, Northwestern University, Evanston, IL, USA.
³ Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
⁴ Integrated Marketing Communications, Medill School of Journalism, Northwestern University, Evanston, IL, USA.
⁵ Department of Psychiatry and Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
⁶ Department of Computer Science, Northwestern University, Evanston, IL, USA.
⁷ Department of Radiology, Northwestern University, Chicago, IL, USA.
⁸ Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA. [email protected].
⁹ Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA. [email protected].
¹⁰ Department of Psychiatry, Massachusetts General Hospital and Harvard School of Medicine, Boston, MA, USA. [email protected].

^# Contributed equally.

Abstract

Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2-3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29-31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.

Abstract

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