Researchers develop new ’emotionally aware’ model for classifying mental health conditions

Researchers have developed a new “emotionally aware” AI-based model for classifying mental health conditions, which could help clinicians better diagnose patients’ mental health conditions. The Emo-MHC model uses machine learning (ML) and deep learning (DL) techniques to analyze text from sources like doctors’ notes, social media posts and online forums to help doctors classify patients’ mental health conditions more accurately and quickly than existing models, which could help provide more effective care.

Many models already use a combination of natural language processing and machine learning to classify mental health conditions, but as many of these rely on self-assessments and standardized clinical tests, they can be inaccurate at classifying mental health conditions.

In some cases, an analysis like this may also miss certain emotional nuances or misinterpret emotions, leading to an incomplete or inaccurate classification, which will in turn affect a patient’s treatment plan and potentially their recovery.

The Emo-MHC model was jointly developed by Dr. Shaily Kabir of the University of Nottingham and Dr. Sangeeta Sangeeta of Keele University, in collaboration with students Joy Paul and Zerin Jahan.

The model is different in that it uses advanced emotion detection and lexicon-based text analysis to reduce the risk of these emotional details being misread or omitted, resulting in more accurate classification of mental health conditions.

The researchers tested the model on publicly available datasets and found it could classify mental health conditions with 92% accuracy, outperforming the benchmark by about 8 percentage points. Their findings are published in the 2026 International Conference on Innovations in Computational Intelligence (ICICI).

The researchers now want to refine the model further to make it even more accurate and assess how it could be used to help more patients dealing with mental health conditions.

Sangeeta, author and lecturer in data science at Keele University, said, “Rates of mental health conditions are increasing, highlighting the urgent need for robust methods that enable accurate early detection. In the era of artificial intelligence and large language models, these technologies have significant potential to support individuals experiencing mental health challenges. Improved diagnostic accuracy can not only reduce the burden on the NHS but also play a critical role in saving lives.”

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Lisa Lock

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Andrew Zinin

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