ALBA: Adaptive Language-Based Assessments for Mental Health

Details

• Personal Author:
  Kjell ONE ; Schwartz HA ; Sikström S ; Varadarajan V
• Description:
  Mental health issues differ widely among individuals, with varied signs and symptoms. Recently, language-based assessments have shown promise in capturing this diversity, but they require a substantial sample of words per person for accuracy. This work introduces the task of Adaptive Language-Based Assessment (ALBA), which involves adaptively ordering questions while also scoring an individual's latent psychological trait using limited language responses to previous questions. To this end, we develop adaptive testing methods under two psychometric measurement theories: Classical Test Theory and Item Response Theory. We empirically evaluate ordering and scoring strategies, organizing into two new methods: a semi-supervised item response theory-based method (ALIRT) and a supervised Actor-Critic model. While we found both methods to improve over non-adaptive baselines, We found ALIRT to be the most accurate and scalable, achieving the highest accuracy with fewer questions (e.g., Pearson r approx. = 0.93 after only 3 questions as compared to typically needing at least 7 questions). In general, adaptive language-based assessments of depression and anxiety were able to utilize a smaller sample of language without compromising validity or large computational costs. [Description provided by NIOSH]
• Subjects:
  Communication Mental Health Nervous System Occupational Health Safety
• Keywords:
  Algorithms Artificial Intelligence Communication Research Machine Learning Mental Health Models
• ISBN:
  9798891761148
• Publisher:
  Association for Computational Linguistics (ACL)
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Proceedings
• Place as Subject:
  New York ; OSHA Region 2
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  1
• NIOSHTIC Number:
  nn:20070699
• Citation:
  NAACL 2024: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), June 16-21, 2024, Mexico City. Kerrville, TX: Association for Computational Linguistics (ACL), 2024 Jun; 1:2466-2478
• Email:
  has}@cs.stonybrook.edu
• Federal Fiscal Year:
  2024
• Performing Organization:
  State University of New York, Stony Brook
• Peer Reviewed:
  False
• Start Date:
  20220701
• Source Full Name:
  NAACL 2024: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), June 16-21, 2024, Mexico City
• End Date:
  20260630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:55d190baace87584006e31472f5d7359edb639ad3b646fada54d67f9b11ef2504748da1eb6fd41c7462c9acdb2ef79b44b1332f197c5912abbff15bc2893bf21
• Download URL:
  https://stacks.cdc.gov/view/cdc/208873/cdc_208873_DS1.pdf
• File Type:
  [PDF - 795.26 KB ]