IKDM 2019
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1st Workshop on

“Intelligent Knowledge Discovery in Medicine”

IISA 2019 (Patras)

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:

  •         representation of knowledge in medicine
  •         decision-support systems based on clinical practice guidelines
  •         patient modeling for effective follow-up
  •         advanced machine learning methods from big data
  •         deep learning in medicine (Biosignals, patient records, etc)
  •         metrics of medical knowledge reliability and transparency
  •         intelligent medical imaging,

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.


1. All accepted papers will be published in Conference Proceedings CD

2. Extended version of accepted papers will be published in the upcoming book: "Medical Knowledge Extraction from Big Data" of NOVA Scientific Publishers.


1. Constantinos Koutsojannis

    Assoc Prof TEI of Western Greece


2. Andreas Andrikopoulos

    Lecturer TEI of Western Greece



3. Vasileios Triantafyllou

    Prof tEI of Westsrm Greece



 4. Haris Matzaroglou

     Assist Prof TEI of Western Greece






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