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
2024.11.21	15:00		Introduction
	15:05	Dr Daeun Lee (Sungkyunkwan University, Seoul)	Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning Bipolar Disorder (BD) is a mental disorder characterized by intense mood swings, from depression to manic states. Individuals with BD are at a higher risk of suicide, but BD is often misdiagnosed as Major Depressive Disorder (MDD) due to shared symptoms, resulting in delays in appropriate treatment and increased suicide risk. While early intervention based on social media data has been explored to uncover latent BD risk, little attention has been paid to detecting BD from those misdiagnosed as MDD. Therefore, this study presents a novel approach for identifying BD risk in individuals initially misdiagnosed with MDD. A unique dataset, BD-Risk, is introduced, incorporating mental disorder types and BD mood levels verified by two clinical experts. The proposed multi-task learning for predicting BD risk and BD mood level outperforms the state-of-the-art baselines. Also, the proposed dynamic mood-aware attention can provide insights into the impact of BD mood on future risk, potentially aiding interventions for at-risk individuals.
	15:40	Kubra Cengiz (Istanbul Technical University)	Cortical morphological networks for profiling autism spectrum disorder using tensor component analysis Atypical neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) can result in changes to cortical morphology at different levels, influencing brain structure and connectivity. In this talk, I will introduce a novel approach using multi-view cortical morphological networks (CMNs), derived from T1-weighted MRI, to study and fingerprint the brain's cortical morphology in ASD compared to neurotypical individuals. This research explores cortical morphology on three levels: individual regions, pairwise region relationships, and multi-view relationships across different cortical attributes such as cortical thickness. Using tensor component analysis, we identify the most representative morphological connectivities shared across CMN views for both the ASD and neurotypical populations. Our findings highlight how specific brain regions, including the temporal, frontal, and insular lobes, are structurally different in ASD individuals, and how these differences correspond to clinical features observed in patients.
	16:20		After talks discussion