IWBBIO 2017 special session
Aims & Scope
Decision making in healthcare at clinical environments is often
made on the basis of multiple parameters and in the context of
patient presentation, which includes the setting and the
specific conditions related to the reason for admission and the
procedures involved. The data used in clinical decision-making
may originate from manifold sources and at multiple scales:
devices in and around the patient, laboratory, blood tests,
omics analyses, medical images, and ancillary information
available both prior to and during the hospitalization.
Arguably, one of the most data dependent clinical
environments is the intensive care unit (ICU). The ICU
environment cares for acutely ill patients. Many patients within
the ICU environments, and particularly surgical intensive care
units (SICU), are technologically dependent on the
life-sustaining devices that surround them. Some of these
patients are indeed dependent for their very survival on
technologies such as infusion pumps, mechanical ventilators,
catheters and so on. Beyond treatment, assessment of prognosis
in Critical Care and patient stratification combining different
data sources is extremely important in a patient-centric
environment.
With the advent and quick uptake of omics
technologies in Critical Care, the use of data-based approaches
for assistance in diagnosis and prognosis becomes paramount. New
approaches to data analysis are thus required, and some of the
most interesting ones are currently stemming from the fields of
Computational Intelligence (CI) and Machine Learning (ML). This
session is particularly interested in the proposal of novel CI
and ML approaches and in the discussion of the challenges for
the application of the existing ones to problems in Critical
Care.
Topics that are of interest to this session
include (but are not necessarily limited to):
·
Novel
applications of existing CI and ML and advanced statistical
methods in Critical Care.
·
Novel CI
and ML techniques for Critical Care.
·
CI and
ML-based methods to improve model interpretability in a Critical
Care context, including data/model visualization techniques.
·
Novel CI
and ML techniques for dealing with non-structured and
heterogeneous data formats in Critical Care.
Organizers
Alfredo
Vellido, PhD.
Dept. of Computer Science, Universitat Politècnica
de Catalunya, BarcelonaTECH (UPC), Barcelona, Spain.
Vicent
Ribas, PhD.
Preliminary dates & Submission Site:
Submission of papers/abstracts by authors (
EXTENDED ) January 4th,
2017.