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
2024.10.17	15:00		Introduction
	15:05	Dr. Angus Addlesee (Amazon Alexa)	Deploying an Accessible LLM-based Conversational Agent in a Hospital We deployed an LLM-based spoken dialogue system in a hospital for over a year. Using generative AI and the ARI robot, real patients and their companions had conversations with our system while waiting for their next appointment. They asked for directions, asked about the cafe menu, asked for jokes, played quizzes, and enjoyed some light distraction from their otherwise stressful day. This multi-party setting in the hospital memory clinic is extremely challenging, requiring computer vision, speech understanding, and gesture generation (arm, head, and eye movements). In this talk, Angus will describe this complex setting and the architecture of the dialogue system. Our system decides when it should take its turn, generates human-like clarification requests when the patient pauses mid-utterance (to improve accessibility for people with dementia), answers in-domain questions (grounding to the in-prompt knowledge), and responds appropriately to out-of-domain requests (like generating jokes or quizzes). This latter feature is particularly remarkable, as real patients often utter unexpected sentences that could not be handled previously.
	15:40	Dr. Baihan Lin (Icahn School of Medicine at Mount Sinai)	Charting Therapeutic Alliances Turn-by-Turn via Language Modeling The therapeutic working alliance is a critical factor in predicting the success of psychotherapy treatment. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this talk, we present COMPASS, a novel framework to directly infer the therapeutic working alliance from the natural language used in psychotherapy sessions. Our approach utilizes advanced large language models (LLMs) to analyze transcripts of psychotherapy sessions and compare them with distributed representations of statements in the working alliance inventory. Analyzing a dataset of over 950 sessions covering diverse psychiatric conditions including anxiety, depression, schizophrenia, and suicidal tendencies, we demonstrate the effectiveness of our method in providing fine-grained mapping of patient-therapist alignment trajectories and offering interpretability for clinical psychiatry and in identifying emerging patterns related to the condition being treated. By employing various deep learning-based topic modeling techniques in combination with prompting generative language models, we analyze the topical characteristics of different psychiatric conditions and their evolution at a turn-level resolution. This combined framework enhances the understanding of therapeutic interactions, enabling timely feedback for therapists regarding the quality of therapeutic relationships and providing interpretable insights to improve the effectiveness of psychotherapy. This talk will also explore pathways for collaboration, particularly in scaling these solutions and applying them to broader health challenges, offering insights into how AI can bridge the gap between data and effective mental health interventions.
	16:20		After talks discussion