Mental Health Assessment and Computational Frameworks

Abstract

Common mental health issues such as depression and anxiety are leading casuse of disability globally. An early and accurate assessment enables people to receive the right care sooner and reduces long-term harm. This paper presents an overview of traditional questionnaires and interviews and explains how new computer- based methods allow for faster, more accurate, and more scalable assessments. Clinician interviews (such as the SCID) are still the reference standard but require time and trained personnel. Self-report instruments (PHQ-9, GAD-7, HADS, K10) are quick and well tested and therefore useful for screening and monitoring. Transferring these tools to phones and the web enables remote use, autoscoring and tracking over time but has demanding privacy and security appended to them. Computer adaptive testing (CAT) can reduce the length of the questionnaire by selecting the most informative questions for each respondent, thus achieving high accuracy at low respondent burden. New approaches use language and data science. Natural language processing (NLP) can be used to analyse short free-text answers and capture numerous details that fixed response options might miss. Computational models which are machine learning based (like support vector machines, random forests, neural networks) can take into account questionnaire score, text and electronic health record data to identify risk, recommend triage and monitor treatment. These tools should be transparent and tested in new settings for fairness because bad data can damage under-represented groups. We must also tackle hindrances to accessing the digital world, like the internet and skills. Clinical judgment shouldn’t be replaced by computational methods but supported. A helpful strategy is to choose relevant tools, digitize with a privacy- by- design focus, combine computer-aided translation (CAT) with free-text modules, develop models with clear reporting and strong validation and fairness checks, and embed outputs into routine clinical workflows. Future work should adapt and personalize screening, join text and speech and biosignals, push cross-cultural testing further, and adopt shared datasets and reporting standards..