Dear Allstat member,
Please be advised that the DSAA Conference running the Data Science in Computational Psychiatry and Psychiatric Research Special Session extended their deadline to June 1st due to many requests.
https://dsaa2018.isi.it/program/special-sessions/comp-psy-ds
Dear Allstat list, I would like to advertise the following exciting special session:
Data Science in Computational Psychiatry and Psychiatric Research<https://dsaa2018.isi.it/program/special-sessions/comp-psy-ds>
Special Session at IEEE International Conference on Data Science <https://dsaa2018.isi.it/home>
and Advanced Analytics 2018<https://dsaa2018.isi.it/home>,
Turin 1.-4. 10 2018
The Precision Medicine and Statistical Learning Group at the Institute of Psychiatry, Psychology and Neuroscience King's College London, and the Data Science & Soft Computing Lab, Goldsmith University London organise the special session Data Science in Computational Psychiatry and Psychiatric Research - CompPsyDS <https://dsaa2018.isi.it/program/special-sessions/comp-psy-ds> Special Session at the IEEE Data Science and Advanced Analytics 2018<https://dsaa2018.isi.it/home> conference in Turin (1.-4.10.2018). Paper submission dateline is 25.5. 20115.
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
Important dates and submission information:
https://dsaa2018.isi.it/calls/call-for-special-session-papers
Kind regards,
Daniel
****************************************************************************
Daniel Stahl, PhD
Reader in Biostatistics
Head of Statistical Learning Group
Department of Biostatistics and Health Informatics, S2.05
Institute of Psychiatry, Psychology & Neuroscience, King's College London
De Crespigny Park, Box PO20
London SE5 8AF
Email: [log in to unmask]<mailto:[log in to unmask]>
https://kclpure.kcl.ac.uk/portal/daniel.r.stahl.html
Statistical Learning Group
www.kcl.ac.uk/statslg<http://www.kcl.ac.uk/statslg>
Department page
http://www.kcl.ac.uk/iop/depts/biostatistics/index.aspx
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