Data Science for Mental Health (DS4MH) @ The Alan Turing Institute
About Us
The vision for this interest group is to kick-start one or more projects using contemporary data science and multi-modal data for mental health to provide insight and benefit for individuals, clinicians, and contribute to fundamental research in mental health (including dementia) as well as the data science methodology. It aims to provide an informal bridge between clinicians, charities, and data owners (like CRIS, UKDP, and Biobank) and data science researchers to stimulate and align cutting edge research in this area.
Events
Meetings
We organise monthly meetings (including half-an-hour long invited talks) at the Turing. Meetings are organised and moderated by Iqra Ali and Yue Wu. Please join our mailing list for more updated information.
As a part of AI UK Fringe, we jointly organised a hybrid event with the NLP interest group on AI for Mental Health Monitoring on 28th March 2024.
See here for our previous talks.
Upcoming Events
Meetings
| Date | Time | Presenter | Title |
|---|---|---|---|
| 2026.03.26 | 15:00 | Introduction | |
| 15:05 | George Fairs (Machine Learning Researcher, thymia) and Stefano Goria (Chief Technology Officer & Co-Founder, thymia) (thymia) |
Voice-Based Psychiatric Assessment in the Real World
We present a multimodal Bayesian network for symptom-level prediction of depression and anxiety from speech and voice data, developed and deployed as a real-world clinical product and evaluated on over 30,000 unique speakers. The model integrates paralinguistic and linguistic features through symptom-specific surrogate models into a joint Bayesian network that learns posterior distributions over individual symptom severities, inter-symptom relationships, and overall condition probabilities (depression ROC-AUC = 0.842, anxiety = 0.831; ECE ≤ 0.018). We examine multimodal integration, cross-channel redundancy, demographic fairness across multiple axes, direct clinician intervention in model predictions, and service user acceptability — arguing that Bayesian networks offer a principled, explainable, and intervenable framework for clinical translation of digital phenotyping. |
|
| 16:00 | After talks discussion |