URL: www.doc.gold.ac.uk/~mas01ds/dssc/DataScienceComputationalPsychiatryPsyResearch.htm
Fionn Murtagh
Director of the Centre for Mathematics and Data Science,
University of Huddersfield, UK
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 include but are not limited to applications of Data Science in:
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
Daniel Stahl – daniel.r.stahl@kcl.ac.uk
Daniel Stamate – d.stamate@gold.ac.uk
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