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
2025.03.20	15:00		Introduction
	15:05	Ruiyi Wang (UCSD)	PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and actual real-world patient practice. To help bridge this gap, we propose PATIENT-𝜓, a novel patient simulation framework for cognitive behavior therapy (CBT) training. To build PATIENT-𝜓, we construct diverse patient cognitive models based on CBT principles and use large language models (LLMs) programmed with these cognitive models to act as a simulated therapy patient. We propose an interactive training scheme, PATIENT-𝜓-TRAINER, for mental health trainees to practice a key skill in CBT – formulating the cognitive model of the patient – through role-playing a therapy session with PATIENT-𝜓. To evaluate PATIENT-𝜓, we conducted a comprehensive user study of 13 mental health trainees and 20 experts. The results demonstrate that practice using PATIENT-𝜓-TRAINER enhances the perceived skill acquisition and confidence of the trainees beyond existing forms of training such as textbooks, videos, and role-play with non-patients. Based on the experts’ perceptions, PATIENT-𝜓 is perceived to be closer to real patient interactions than GPT-4, and PATIENT-𝜓-TRAINER holds strong promise to improve trainee competencies. Our code and data are released at https://github.com/ruiyiw/patient-psi.
	15:40	Lujain Ibrahim (University of Oxford)	Multi-turn evaluation of anthropomorphic behaviors in large language models The tendency of users to anthropomorphise large language models (LLMs) is of growing interest to AI developers, researchers, and policy-makers. In this work, we present a novel method for empirically evaluating anthropomorphic LLM behaviours in realistic and varied settings. We find that all SOTA LLMs evaluated exhibit similar behaviours, characterised by relationship-building (e.g., empathy and validation) and first-person pronoun use. Our work lays an empirical foundation for investigating how design choices influence anthropomorphic model behaviours and for progressing the ethical debate on the desirability of these behaviours in social applications like those for mental health services and companionship.
	16:20		After talks discussion