Connectionists: CFP: IJCNN'17 Special Session: ML for ENHANCING BIOMEDICAL DATA ANALYSIS

[Alfredo Vellido]

Apologies for cross-posting
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1st CALL FOR PAPERS
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IEEE IJCNN 2017 Special Session on
MACHINE LEARNING for ENHANCING BIOMEDICAL DATA ANALYSIS

May 14-19, 2017, Anchorage, Alaska, USA.

www.cs.upc.edu/~avellido/research/conferences/IJCNN2017-ssEnhancedBiomed.html

Aims & Scope
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The pursuit of precision medicine has led to an explosive growth in the 
amount of data now available.  Ever-increasing advances in technology 
and greater levels of granularity have also resulted in an increase in 
the amount of data and complexity of knowledge surrounding disease.  The 
increased demand for skilled analysts in systems medicine and 
engineering has, unfortunately, outpaced the supply.

In spite of these shortcomings, productivity in systems medicine and 
engineering is nevertheless growing.  Results of these efforts will 
enable practitioners to improve diagnoses and treatments, and allow 
health care systems to better manage patients and reduce costs.  Central 
Data analysis is also crucial to get closer to a real personalized 
medicine, one of the main goals of modern society in terms of healthcare.

This special session aims at bringing together researchers from the 
fields of Biomedicine and Machine Learning (ML) in order to exploit the 
synergies between them, thus taking advantage of the modeling 
capabilities of ML and the expert knowledge in Biomedicine to make 
progresses truly relevant to the medical community by focusing on the 
solution of real problems, whose results hopefully lead to palpable 
enhancements in clinical routine practice and in increasingly 
personalized medicine. The proposal of any method useful for medical 
data analysis, even if it does not fall completely within ML (e.g., 
biostatistics or biomedical signal processing), is also welcome in this 
special session. In particular, we welcome papers which present novel 
algorithms or refined classical methods applied to biomedical problems. 
These include but are not limited to:

- Proposals of new ML algorithms that outperform previous approaches in 
clinical problems.
- Practical applications of computational intelligence and ML for mining 
health-related data.
- Structure finding, including efficient derivation of directed graphs 
with applications to extract probabilistic graphical relationships 
between features in biomedical problems.
- Integration of expert clinical knowledge in graphical models.
- Methodologies for fusion of heterogeneous data: clinical tests, 
subjective assessments, molecular biomarkers, histology, imaging, 
electrophysiological measurements, etc.
- Models of time-to-event data to characterize prognostic outcomes and 
treatment effects.
- Methodologies for medical decision aid and treatment planning.
- Telemedicine and proposals for a remote healthcare system.

Important Dates
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Paper submission: November 15, 2016
Paper decision notification: January 20, 2017
Camera-ready submission: February 20, 2017

Session Chairs
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José D. Martín-Guerrero (Universitat de Valencia, Spain)
Paulo J.G. Lisboa (P.J.Lisboa at ljmu.ac.uk , Liverpool John Moores 
University, U.K.)
Alfredo Vellido (Universitat Politècnica de Catalunya, Spain)
Azzam F.G. Taktak (University of Liverpool, U.K.)
Leif E. Peterson (Houston Methodist Biostatistics Core, TX, U.S.A.)