Special session Machine learning in psyhicaitry at IEEE ICMLA Dec 2017,
From firstname.lastname@example.org@21:1/5 to All on Tue Jul 18 10:43:27 2017
Call for papers
We invite submissions for a special session “Machine/statistical Learning in Psychiatric Research” at this year’s 16th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'17), Cancun, Mexico, 18-20 December. http://www.
The aim of this session is 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. Statisticians applying statistical learning are especially welcome!
PAPER SUBMISSION DEADLINE is August 6, 2017
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, proteomic and other -omics 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
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
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
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!