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 Jenny Chim, Yue Wu, and Emilio Ferrucci. 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
2025.01.16	15:00		Introduction
	15:05	Vivian Nguyen & Dave Jung (Cornell University)	Taking a turn for the better: Conversation redirection throughout the course of mental-health therapy Mental-health therapy involves a complex conversation flow in which patients and therapists continuously negotiate what should be talked about next. For example, therapists might try to shift the conversation's direction to keep the therapeutic process on track and avoid stagnation, or patients might push the discussion towards issues they want to focus on. How do such patient and therapist redirections relate to the development and quality of their relationship? To answer this question, we introduce a probabilistic measure of the extent to which a certain utterance immediately redirects the flow of the conversation, accounting for both the intention and the actual realization of such a change. We apply this new measure to characterize the development of patient-therapist relationships over multiple sessions in a very large, widely-used online therapy platform. Our analysis reveals that (1) patient control of the conversation's direction generally increases relative to that of the therapist as their relationship progresses; and (2) patients who have less control in the first few sessions are significantly more likely to eventually express dissatisfaction with their therapist and terminate the relationship.
	15:40	Nivedita Bijlani (University of Surrey)	Smart Sensing in Dementia Care: Lightweight, Explainable AI Validated for Early Warning of Health Events in the Home Sensor-based remote health monitoring for people living with dementia in their home enables the early detection of adverse health events, reducing the risk of hospitalisation. However, real-world home sensor data present challenges such as noise, sparse and imprecise labelling, inter-household variability, and the need for clinical explainability. To address these challenges, we developed a lightweight, explainable AI pipeline for anomaly detection in home sensor data, aimed at facilitating early health event detection. Validated in an ongoing real-world dementia monitoring study, the pipeline employs a self-supervised contrastive learning model to generate noise-resilient daily representations, compute anomaly scores, and trigger alerts based on household-personalised thresholds. Novel 'spatiotemporal attention maps' provide insights into the source and timing of anomalies, offering clinicians interpretable insights into atypical behaviour patterns. Additionally, LLM-powered anomaly summaries enhance clinical interpretability. Validation on a 90-patient cohort (40,866 person-days; May 2022 - Feb 2024) demonstrated high sensitivity and generalisability. The system was developed for the Minder Research Study, launched in August 2019 by the UK Dementia Research Institute Care Research and Technology Centre at Imperial College London, in partnership with the University of Surrey, and Surrey and Borders Partnership NHS Trust.
	16:20		After talks discussion