Accessibility settings

This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (4887) Clinical Information and Decision Making (3509) Depression and Mood Disorders; Suicide Prevention (2384) Longitudinal and Cohort Studies in Public Health (882) Machine Learning (3023) Mental Health Issues in Elderly Patients and Geriatric Psychiatry (293)

Published on in Vol 10 (2026)

This is a member publication of University of Surrey (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/84744, first published .
Elderly woman frowning while looking at phone in bedroom

Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing

Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing

Authors of this article:

Bahar Khorram1 Author Orcid Image ;   Ramin Nilforooshan2, 3, 4 Author Orcid Image ;   Payam Barnaghi4, 5, 6 Author Orcid Image ;   Samaneh Kouchaki1, 4 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Bahar Khorram   1 , MSc ;   Ramin Nilforooshan   2, 3, 4 , MD ;   Payam Barnaghi   4, 5, 6 , PhD ;   Samaneh Kouchaki   1, 4 , PhD

1 Centre for Vision, Speech and Signal Processing (CVSSP), Department of Electrical and Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, United Kingdom

2 Surrey and Borders Partnership NHS Foundation Trust, Leatherhead, United Kingdom

3 School of Psychology, University of Surrey, Guildford, United Kingdom

4 UK Dementia Research Institute, Care Research and Technology Centre, London, United Kingdom

5 Department of Brain Sciences, Imperial College London, London, United Kingdom

6 Department of Data Research, Innovation and Virtual Environments, Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom

Corresponding Author:

  • Bahar Khorram, MSc
  • Centre for Vision, Speech and Signal Processing (CVSSP), Department of Electrical and Electronic Engineering
  • Faculty of Engineering and Physical Sciences, University of Surrey
  • Stag Hill Campus
  • Guildford GU2 7XH
  • United Kingdom
  • Phone: +44 (0)1483 683435
  • Email: bk00531@surrey.ac.uk