XML Research Seminar: Personalised Treatment for Mental Health

Title:
Personalised Treatment for Mental Health

Abstract:
The integration of Artificial Intelligence (AI) in healthcare represents a promising frontier, especially in the domain of mental health. Advancements in machine learning and data analytics are contributing to a nuanced understanding of individual symptomatology, promoting tailored therapeutic interventions that are both effective and efficient. These technologies offer potential to transcend the 'one-size-fits-all' approach that characterises much of current mental health practice. This talk covers some of our recent projects, how these tools can facilitate personalisation in treatment strategies, in order to improve treatment outcomes for those living with mental health conditions.

Bio:
Qiang Liu is a lecturer in the Department of Engineering Mathematics, University of Bristol. He is also an honorary research scientist in the Department of Psychiatry, University of Oxford. Prior to these, he was a postdoc researcher in the Department of Psychiatry, University of Oxford. He received his PhD in Computer Science from the University of Essex, UK.

He has been working on mental health and healthcare robotics. He has cross-disciplinary interests in mental health illnesses, neuroscience and AI. His aim is to develop and apply state-of-the-art AI and bioinformatic techniques to gain a deeper understanding of neurological disorders and to develop effective treatments. His research interests include machine learning algorithms especially deep neural networks; personalised treatment and diagnosis; prediction and inference modelling; drug discovery; cell profiling; brain imaging, genomic and EHR data analysis; smart sensors, wearable sensors, sensor integration and data fusion algorithms; MCDA in healthcare; biomedical signal processing; natural language processing.

* This is a hybrid event. You can also join remotely. Please see Teams link on the OxfordXML webpage (see URL provided in Further Information).