Knowledge Discovery and Data Mining

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Understanding the impact that data mining continues to have on health care.
Posted On: December 18, 2018
Image Caption: 
Conference attendee and postdoctoral fellow, Dr. Serene Wong (pictured) works in the laboratory of Krembil Research Institute Senior Scientist Dr. Igor Jurisica at University Health Network.

Conference: 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), August 19th-23rd, London, United Kingdom.

Conference Highlight: As one of the premier conferences in knowledge discovery and data mining, KDD 2018 featured cutting-edge research and developments in academia and industry.

Conference Summary: This year’s conference was packed with an array of interesting events and activities including workshops, tutorials, keynote speakers and applied data science talks. The program also included health day and deep learning day programs.

Health is an important domain for the knowledge discovery and data mining community. Topics of interest in the health day program included data mining in epidemiology, machine learning for medicine and health care, deep learning for computational health care and knowledge discovery from electronic health records. With artificial intelligence (AI) methods’ increasing popularity, the health day program dedicated an entire tutorial to AI and its role in health care. During the tutorial, an interesting example involving the prediction of which group of pneumonia patients should be admitted to the hospital was used to demonstrate how important it is to have explainable AI models in health care.

It is also encouraging to see that AI algorithms on health data provided promising results. For example, researchers developed an algorithm that could generate potential drug molecules based on a prototype drug. Currently, researchers are collaborating with pharmaceutical companies to test these drug molecules. 

Overall, this conference helped show how influential AI has been and will continue to be in health care.