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Dear all,
The 5th International Workshop on Knowledge Discovery in
Healthcare Data will be held during ECAI 2020 in Santiago de
Compostela (Spain) on June 8 or 9, 2020.
Important Deadlines:
• Technical Paper: March 6th, 2020
• BGLP Challenge: March 24th, 2020
There are many healthcare datasets consisting of both structured
and unstructured information, which provide a challenge for
artificial intelligence and machine learning researchers seeking
to extract knowledge from data. Existing healthcare datasets
include electronic medical records, large collections of complex
physiological information, medical imaging data, genomics, as well
as other socio-economic and behavioral data. In order to perform
data-driven analysis or build causal and inferential models using
these datasets, challenges such as integrating multiple data
types, dealing with missing data, and handling irregularly sampled
data need to be addressed. While these challenges must be
considered by researchers working with healthcare data, a larger
problem involves how to best ensure that the hypotheses posed and
types of knowledge discoveries sought are relevant to the
healthcare community. Clinical perspectives from medical
professionals are required to ensure that advancements in
healthcare data analysis result in positive impact to
point-of-care and outcome-based systems.
This workshop builds upon the success of previous Knowledge
Discovery in Healthcare Data (KDH) workshops. It welcomes
contributions providing insight on the extent to which AI
techniques have successfully penetrated the healthcare field,
interaction among AI techniques to achieve successful learning
healthcare systems, and distinctions between AI and non-AI models
needed in modern healthcare environments. The focus of the
workshop is on issues in data extraction and assembly, knowledge
discovery, decision support for healthcare providers, and
personalised self-care aids for patients. A workshop highlight
will be the Blood Glucose Level Prediction (BGLP) Challenge, in
which researchers will compare the efficacy of different machine
learning prediction approaches on a standard set of data from
patients with type 1 diabetes.
Main Topics:
• Knowledge discovery and data analytics
• Data extraction, organization and assembly
• Personalisation and decision support
• Blood glucose level prediction
A full list of topics and submission guidelines can be found at:
https://sites.google.com/view/kdh-2020
Kerstin Bach, Cindy Marling, Nirmalie Wiratunga
Workshop Organizers