1st International Workshop on Data Mining for Mental Disorders held at the
23rd IEEE International Conference on Data Mining (ICDM’23)
1 December 2023, Shanghai, China
The prevalence of mental illnesses is rapidly increasing, with approximately 20-25% of individuals experiencing these disorders during their lifetime. This rise has been further exacerbated within the Covid-19 pandemic. Depression, anxiety, and substance or alcohol abuse related disorders are among the most frequent mental health conditions affecting individuals. Mental illnesses not only cause significant personal burden but also have substantial economic costs due to sick-leaves and early retirements. The early onset of mental illness, particularly during childhood and adolescence, is critical as it can interfere with essential developmental tasks such as building social relationships, developing of an autonomous personality and academic achievements. Furthermore, it is associated with a high probability of chronification and more severe courses of diseases until adulthood. Unfortunately, despite the increasing prevalence of mental illnesses, several shortcomings due to structural and personal barriers, the supply, uptake, and effectiveness of mental health treatments remain modest, resulting in high numbers of non-responders or relapse.
It is crucial to address this gap between the increasing need for mental health services and the availability of and access to effective treatments – ideally for all, anytime, and anywhere. Therefore, we must explore innovative data mining solutions to improve timeliness, access to, and the effectiveness of mental health treatments. This can involve a collaboration between Artificial Intelligence solutions based on current and next generation data mining and mental health professionals, researchers, policymakers, and other stakeholders to discuss evidence-based practices, emerging technologies, and policy recommendations.
In this light, the workshop will explore data mining for mental disorder, aiming to solicit highest quality contributions with, but not limited to:
- Data Mining for Mental Disorder systems based on textual, audiovisual, biochemical, electrophysiological, photoelectric, physical data, and their multimodal combination
- Data Mining for prevention and treatment of mild and severe mental disorders
- Foundation Models and their tuning in mental disorders
- Transfer, active, drifting target, federated, few-shot, and self/semi-supervised learning for mental disorders
- Dependable, explainable, fair, green, and reliable mental disorder modelling
- Cooperative human/AI data mining approaches for mental disorders
- Mobile and ubiquitous data mining for mental disorders
- Applications in alcohol and further addictions, Alzheimer’s disease, anorexia nervosa, autism, bipolar disorder, dementia, depression, negative emotions, suicidal risk, and beyond
- Datasets and benchmarks for Data Mining in Mental Disorders
- Surveys, systematic overviews, and blue-sky contributions for Data Mining in Mental Disorder
The impacts of mental illnesses are far-reaching, and we need to work together to find solutions that can make a real difference in the lives of individuals struggling with mental health conditions. Your expertise and perspectives will be invaluable to the success of this workshop. By bringing together a diverse group of experts and stakeholders, we hope to develop actionable solutions that can improve mental health services and resources.
We invite you to join us in this critical discussion and contribute to the development of innovative data mining solutions to address the increasing prevalence of mental disorders.
Dates
15 September 2023 – Paper submission deadline (extended)
24 September 2023 – Notification of acceptance to authors (shifted to align with all ICDM workshops)
1 October 2023 – Camera-ready submission deadline and copyright form
1 December 2023 – Workshop held in Shanghai, China @ICDM 2023
Contribute
Submit your paper online.
Please follow the submission guidelines for paper preparation provided at the main conference.
Registration Fee: The registration fee of a workshop paper is the same as that of a main conference paper. Please refer to the registration webpage of the main conference for the fee details. There is no extra page fee for all workshop papers.
Online Attendance Option: In light of potential hesitancy for international traveling, ICDM is considering providing an online attendance option for the ICDM workshops (including DMMD). This would cater to those who may face travel restrictions or have concerns about in-person attendance.
Program
Friday, 1 December 2023
Time (Beijing Time) Title, Presenter/Author
14:30-14:40 Opening Remarks, Organizers
14:40-14:55 Crossmodal Transformer on Multi-Physical Signals for Personalised Daily Mental Health Prediction, Meishu Song, Zijiang Yang, Andreas
Triantafyllopoulos, and Yamamoto Yoshiharu
14:55-15:10 An End-to-End Learning Model for Detection of Psychiatric Diseases via Spontaneous Physical Acitivity Data, Dewen Xu, Zhihua Wang, Tsuyoshi Kitajima, Toru Nakamura, Hiroko Shimura, Hiroki Takeuchi, Yang Tan, Runze Ge, Kun Qian, Bin Hu, Bjoern W. Schuller, and Yamamoto Yoshiharu
15:10-15:30 Annotating Panic in Social Media using Active Learning, Transformers and Domain Knowledge, Sandra Mitrovic, Fabio Frisone, Suryam Gupta, Chiara Lucifora, Dragana Carapic, Carlo Schillaci, and Samuele Di Giovanni
15:30-15:45 Artificial Intelligence on Mental Health Context, Faustino Muetunda, Soumaya Sabry, Sebastião Pais, Gaël Dias, João Cordeiro, and Nuno Pombo
15:45-16:00 Improving Mental Disorder Predictions using Feature-Based Machine Learning Techninques, Faustino Muetunda, Soumaya Sabry, Sebastião Pais, Gaël Dias, João Cordeiro, and Nuno Pombo
16:00 Closing Remarks, Organizers
Organisers
Björn W. Schuller, Imperial College London, UK
Johanna Löchner, University of Tübingen, Germany
Kun Qian, Beijing Institute of Technology, China
Zichao Nie, Beihang University, China
Bin Hu, Beijing Institute of Technology, China
Yoshiharu Yamamoto, The University of Tokyo, Japan
Program Committee
Oliver Amft, University of Freiburg, Germany
Katrin Bartl-Pokorny, Medical University of Graz, Austria
Anton Batliner, University of Augsburg, Germany
Harald Baumeister, University of Ulm, Germany
Björn Eskofier, FAU, Germany
Anna Esposito, University of Campania, Italy
Jing Han, University of Cambridge, UK
Haifeng Li,HIT, China
Shrikanth Narayanan, USC, USA
Florian Pokorny, Medical University of Graz, Austria
Matthew Purver, QMUL, UK
Zhao Ren, University of Bremen, Germany
Fabien Ringeval, Université Grenoble Alpes, France
Dongrui Wu, GE, USA
Zixing Zhang, Hunan University, China
Ziping Zhao, TJNU, China
Jiayu Zhou, Michigan State University, USA