IJCNN 2017 Special Session on
Machine Learning for Enhancing Biomedical Data Analysis
Aims & ScopeThe
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
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 Paper
submission: November 15,
December 1, 2016
Paper decision notification: January 20, 2017
Camera-ready submission: February 20, 2017
Session Chairs José D. Martín-Guerrero
(Universitat de Valencia, Spain)
Paulo J.G. Lisboa (P.J.Lisboa@ljmu.ac.uk , Liverpool John
Moores University, U.K.)
Alfredo Vellido (Universitat Politècnica de Catalunya,
Azzam F.G. Taktak (University of Liverpool, U.K.)
Leif E. Peterson (Houston Methodist Research Institute,