Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 27, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Calibrated Deep-Learning Risk Indexing and Latent Behavioural Profiling for Occupational Mental-Health Risk

Abuzar Khan¹, Khalid Rehman², Ahmad Junaid¹

¹Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar 25100, Pakistan.

Bioengineering (Basel, Switzerland)

|June 26, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.

Frontiers in neuroinformatics·2026

Same author

Explainable Text-Based Depression and Suicide Risk Prediction from Social Media Using Deep Learning and Graph Neural Networks.

Healthcare (Basel, Switzerland)·2026

Same author

Characterization of a transmembrane-activating STING agonist using genetically humanized mice.

Cell reports·2026

Same author

Reply to Rhazzar et al. The Impact of Author Name Ambiguity on Bibliometric Validity. Comment on "Ejaz et al. Bibliometric Analysis of Publications on the Omicron Variant from 2020 to 2022 in the Scopus Database Using R and VOSviewer. <i>Int. J. Environ. Res. Public Health</i> 2022, <i>19</i>, 12407".

International journal of environmental research and public health·2026

Same author

Bibliometric Trends in the Integration of Computer Vision With Healthcare.

Healthcare technology letters·2026

Same author

Smart sensing-enabled risk-aware nitrogen prescriptions via conformal profit bounds for precision agriculture.

Frontiers in plant science·2026

Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026

Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026

Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026

Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026

Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026

Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026

See all related articles

This summary is machine-generated.

This study introduces a computational framework to assess occupational mental health risks in knowledge workers, providing a transparent risk index for better support and resource allocation. The model demonstrates strong performance and adaptability across different datasets.

Area of Science:

Computational Intelligence
Public Health
Occupational Psychology

Background:

Occupational mental health is a significant concern in knowledge work due to factors like workload and limited support.
Current workplace well-being assessments lack interpretability and subgroup evaluation.
Early-career academics serve as a model context for addressing psychological risks in professional settings.

Purpose of the Study:

To develop a computational-intelligence framework for public mental health decision support using heterogeneous workplace survey data.
To create a transparent, calibrated risk index for preventive outreach and psychosocial support planning.
To enhance interpretability, subgroup evaluation, and transfer validation of mental health risk models.

Main Methods:

Keywords:

computational intelligence for healthcare ethical AI health disparities knowledge work latent profiling occupational mental health psychosocial support public mental health risk index transfer learning workplace survey data

Related Experiment Videos

Last Updated: Jun 27, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Combined attention-based tabular learning, variational autoencoder latent profiling, and stacked ensemble prediction.

Incorporated probability calibration, feature attribution, perturbation analysis, and fairness assessment.

Utilized cross-dataset adaptation for transfer validation on an occupational burnout dataset.

Main Results:

Achieved strong held-out performance (AUC=0.885, AP=0.872) and outperformed baseline models.
Demonstrated robustness with a mean test AUC of 0.809±0.044 across five folds.
External evaluation on a burnout dataset yielded high performance (AUC=0.941, AP=0.936) after adaptation.

Conclusions:

The developed framework provides calibrated, interpretable, and subgroup-aware decision support for occupational mental health.
Key predictors identified include work interference, perceived stress, and access to care.
The model's adaptability supports effective decision-making even under dataset shift, enhancing public mental health initiatives.