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. |
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| 15:40 | Dr. Baihan Lin (Icahn School of Medicine at Mount Sinai) |
TBA
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| 16:20 | After talks discussion |