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Statistical Learning Group

Special Session "Machine Learning in Psychiatric Research" at ICMLA 2017

The UK “Prediction Modelling in Psychiatric Research Group” organizes a

Special session on "Machine learning in Psychiatric Research" at the 16th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'17), Cancun, Mexico, 18-20 December 2017.


***Update***: New Extended Paper Submission Deadline August 26th, 2017


Psychiatric research entered the age of big data with patient databases now available with thousands of clinical, demographical, social, environmental, neuroimaging, genomic, proteonomic and other -omic measures.

The analyses of such data is often more challenging than in other medical research areas because i) psychiatrists study traits which are not easily measurable; they need to be measured indirectly e.g. by questionnaires, ii) the definition of a mental desease is often very broad and often includes distinct but unknown subcategories, iii) there is a high proportion of drop-out in many studies and patients often do not adhere to the treatment and iv) treatment interventions often have several interacting and it is often difficult to measure components (complex interventions). Psychiatric research therefore presents special problems for researchers in addition to the standard methodological challenges, such as the number of variables exceeding the number of patients.

Machine learning techniques are increasingly being used to address problems in psychiatric and psychological research, including bioinformatics, neuroimaging, prediction modelling and personalized medicine, causal modelling, epidemiology and many other research areas.

We would like to invite researchers from both academia and industry to participate in this workshop to present, discuss, and share the latest findings in the field, and exchange ideas that address real-world problems with real-world solutions, as well as to discuss future research directions.

This special session is open to all interested persons. We especially welcome psychiatrists and psychologist who apply or plan to apply machine and statistical learning methods to their research. Topics relevant in this workshop include but are not limited to:
Applications of Data Science in

  • Prediction models of differential treatment success (Personalized medicine)
  • Development of diagnostic, risk and prognostic models
  • Big data and highly dimensional data analysis in psychiatric research
  • Improving apparent validity of prediction models
  • Methods for prediction and knowledge discovery from Electronic Health Record (EHR) data
  • Adaptive clinical trials and machine learning
  • Causal modelling, including Mendalian Randomization
  • Neuroimaging, EEG and ERP studies
  • Bioinformatics and -omics studies
  • Modelling selection bias in case-control studies
  • Machine learning application to reduce the problem of selective inference and low reproducibility of research studies
  • Methods for predicting from streaming activity and other data from wearable sensor data and real-time prediction methods (“mobile health”)
  • Handling informative missing or censored outcome data
  • Identifying subgroups of patients with schizophrenia, depression or other mental health problems

Submission of papers

Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from IEEE website. The maximum length of papers is 8 pages.Please refer to the conference webpage for initial submission and updates. Poster submissions are also welcome!


Important Dates:

New Extended Paper Submission Deadline August 13th, 2017

Notification of Acceptance September 9th, 2017

Camera-Ready Papers October 1st, 2017


If you have any questions about this session, please do not hesitate to email them to


Special Session Chairs

Dr. Daniel Stahl  

Department of Biostatistics and Health Informatics, Institute of Psychology, Psychiatry and Neuroscience, King’s College London, UK


Dr. Daniel Stamate

Department of Computing. Goldsmiths, University of London, UK


Program Committee Members

Prof. Fionn Murtargh, Dep. of Computing, Goldsmiths, University of London, UK

Dr. Raquel Iniesta, Dep. of Medical & Molecular Genetics, King’s College London, UK

Dr. Danielle Belgrave,  Dep. of Medicine, Imperial College London, UK

Dr. Sinan Guloksuz, Dep. of Psychiatry and Neuropsychology, Maastricht University Medical Centre, Nethrlands, and Dep. of Psychiatry, Yale School of Medicine, USA

Dr. Cedric Ginestet, Dep. of Biostatistics and Health Informatics, King's College London, UK

Prof. Richard Emsley, Division of Population Health, Health Services Research & Primary Care, University of Manchester, UK

Dr. Mattias Pierce,  Division of Population Health, Health Services Research & Primary Care, University of Manchester, UK



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