The 5th IEEE International
Conference on Data Science
and Advanced Analytics

1–4 October 2018
Turin — Italy

Data Science in Computational Psychiatry and Psychiatric Research


10:30 - 12:30, room “Torino”
  • 10:30Opening remarks
    Daniel Stahl and Daniel Stamate
  • 10:35Keynote presentation: “Analysis of Activities, Contextualized for General Health, Depression and Demographics”
    Fionn Murtagh, Director of the Centre for Mathematics and Data Science, University of Huddersfield, UK
  • 11:10“Leveraging Latent Dirichlet Allocation in Processing Free-Text Entries of Personal Goals among Patients Undergoing Bladder Cancer Surgery”
    Yuelin Li and Thomas M. Atkinson
  • 11:30“Outcome-Weighted Learning for Personalized Medicine with Multiple Treatment Options”
    Xuan Zhou, Yuanjia Wang and Donglin Zeng
  • 11:50“MixDir: Scalable Bayesian Clustering for High-Dimensional Categorical Data”
    Constantin Ahlmann-Eltze and Christopher Yau
  • 12:10“MRI-based Diagnostics of Depression Concomitant with Epilepsy: in Search of the Potential Biomarkers”
    Maxim Sharaev, Alexey Artemov, Alexander Andreev, Alexander Bernstein, Evgeny Burnaev, Ekaterina Kondratyeva, Svetlana Sushchinskaya and Irina Samotaeva

Keynote Speaker

Fionn Murtagh
Director of the Centre for Mathematics and Data Science,
University of Huddersfield, UK

Aims and Scope

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 analysis 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 disease 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. Machine learning plays also an important role in the definition of the modern field of Computational Psychiatry.

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 and applications. This special session is open to all interested persons.

Topics of Interest

Topics of interest include but are not limited to applications of Data Science in:

  • Computational Psychiatry
  • Prediction models of differential treatment success (Personalized medicine)
  • Development of diagnostic, risk and prognostic models (e.g. predicting risk of dementia, psychosis, etc)
  • 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 Mendelian 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
  • Machine learning and the development of measurement scales


Daniel Stahl
Department of Biostatistics and Health Informatics,
King’s College London, UK

Daniel Stamate
Data Science & Soft Computing Lab, and Department of Computing Goldsmiths,
University London, UK

Contact Persons

Daniel Stahl –
Daniel Stamate –

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