Machine Learning for the Diagnosis and Treatment of Affective Disorders (ML4AD)
CfP: ACII 2019 WORKSHOP, 3rd SEPTEMBER, CAMBRIDGE UK
In recent years, there has been an increase in interest and explorations of the use of machine learning (ML) to assist in the diagnosis of mental health problems; and for improving access to, engagement with, and the outcomes of, therapeutic treatment. Research has started to explore the identification of mental health problems through inferences about peoples’ behaviours on social media, online searches, or mobile phone app uses; as well as varied approaches to assess, or continuously monitor, a person’s mental health and related symptoms by measuring sleep, mood, stress, social or physical activity via audio, visual or physiological signal processing. Despite great potential, the realization of effective ML-enabled applications for mental health remains a hugely challenging area for research and development.
This workshop will bring together an inter-disciplinary group of researchers and practitioners from academia and industry to discuss the unique opportunities and challenges for developing effective, ethical and trustworthy ML- approaches and interventions for the diagnosis and treatment of affective disorders.
The special focus will be on (but it is not limited to):
- Examples of ML-algorithms and ML-enabled applications for detecting, monitoring, or predicting peoples’ mental health and wellbeing using static, longitudinal and/or iterative data (e., online learning)
- Approaches to passive sensing and signal processing of visual, audio, physiological or multi-modal inputs for assessing human emotions and behaviours related to affective disorders
- Design challenges for interfaces and interactions that incorporate ML for affective disorders.
- ML approaches to assist precision in healthcare, predict health risks, the discovery of disease subtypes, or the development of personalized interventions in the context of affective disorders
- Practical challenges in conducting ML research to aid affective disorders in real-world situations (i.e., gaining access to users and data; prototyping ML systems end-to-end; working effectively in multi-disciplinary teams)
- Human-in-the-loop and collaborative approaches in the design of ML interventions that aim to support clinical decision making
- Reflections on ethics in developing ML interventions for the diagnosis and treatment of affective disorders
Submissions due: 18th of June 2019
Notification: 9th of July 2019
Camera Ready papers: 12th July 2019
We invite submissions of short papers (2-4 pages) in ACII paper format. Submissions will be reviewed by members of the organising and program committee. Workshop proceedings will be published by IEEE Xplore.
Anja Thieme – HCI Researcher, Microsoft Research Cambridge, UK
Danielle Belgrave – ML Researcher in Healthcare, Microsoft Research Cambridge, UK
Gavin Doherty – Associate Professor in Computer Science and Statistics, Trinity College Dublin, IRL
Tad Hirsch – Professor of Art + Design, Northeastern University, USA
Munmun De Choudhury – Assistant Professor in Interactive Computing, Georgia Institute of Technology, USA
Mary Czerwinski – Research Manager of Visualisation and Interaction, Microsoft Research Redmond, USA
Akane Sano – Assistant Professor, Electrical Computer Engineering and Computing Science, Rice University, USA