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IISA | IKDM: Intelligent Knowledge Discovery in Medicine
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IKDM: Intelligent Knowledge Discovery in Medicine

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Knowledge Discovery (KD) is the process of extracting information from a large volume of data. A multi-disciplinary field of science and technology, includes statistics, database systems, computer programming, machine learning, and Artificial Intelligence (AI). Knowledge is at the heart of healthcare processes and comes in various forms: definitions of medical terms, relationships between symptoms and diseases, guidelines for treating patients, models for making rational decisions etc. These essential issues involve the nature of medical knowledge, medical guidelines, and assessment of AI employment in healthcare decision making.

Contact Us: ckoutsog@teiwest.gr

Background and Goals

Knowledge Discovery (KD) is the process of extracting information from a large volume of data. A multi-disciplinary field of science and technology, includes statistics, database systems, computer programming, machine learning, and Artificial Intelligence (AI). Knowledge is at the heart of healthcare processes and comes in various forms: definitions of medical terms, relationships between symptoms and diseases, guidelines for treating patients, models for making rational decisions etc. These essential issues involve the nature of medical knowledge, medical guidelines, and assessment of AI employment in healthcare decision making.

The objectives of this workshop are:

reasoning and decision making
representation of knowledge in medicine
decision-support systems based on clinical practice guidelines
patient modeling for effective follow-up
advanced machine learning methods
deep learning in medicine (Biosignals, Medical Imaging, etc)
metrics of medical knowledge reliability and transparency,

Topics of Interests

This workshop including keynote speeches, as well as throughout successful applications of AI in medical KD from different areas as:

(a) Clinical Medicine: KD from clinical centers acting as a mass database and a source of complex clinical, laboratory, equipment use, and drug management data which can be analyzed for disease diagnosis and decision making;

(b) Public Health: KD for early outbreak detection and healthcare surveillance;

(c) Healthcare Text mining: KD from mining medical literature, as well as mining clinical data such as patients’ clinical records; and

(d) Healthcare Planning: KD from clinical profiles among patients diagnosed illness which has extra treatment burden.

equips participants with conceptual tools to understand essential issues in medical AI concerning with representation of knowledge and provides skills for working with tools that employ these concepts.

Call for Papers
Chairs

Constantinos Koutsojannis
Assoc Prof TEI of Western Greece
ckoutsog@teiwest.gr

Greece

Important Dates
Program Committee

Ioannis Hatzilygeroudis

Prof Univ of Patras

ihatz@ceid.upatras.gr

Greece

Foteini Grivokostopoulou

Lecturer TEI of Western Greece

grivokwst@ceid.upatras.gr

Greece

Isidoros Perikos

Lecturer TEI of Western Greece

perikos@ceid.upatras.gr

Greece

Instructions for Authors

Contact Us: ckoutsog@teiwest.gr