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Wednesday, March 14, 2018

Talk: AI for Decision Support in Health - how to make it work

Upcoming CSIG Meeting:

Date and Time :  Mar 15, 2018 - 10:30am US Eastern
Zoom meeting Link: https://zoom.us/j/7371462221
Zoom Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221
Zoom International Numbers: https://zoom.us/zoomconference
Website: http://cognitive-science.info/community/weekly-update/  (Slides, Recording)

{ Also presented at #OpenTechAI workshop in Helsinki https://developer.ibm.com/opentech/2018/01/29/helsinki-march-2018-opentech-ai-workshop/ }

Talk Title: AI for Decision Support in Health - how to make it work
Presenters: Mark van Gils (VTT)

Mark van Gils is an experienced research & development professional, specializing in data-analysis solutions for health and wellbeing applications. A successful track-record in setting-up, carrying out and managing data-analysis projects with healthcare professionals and SMEs and global companies operating in the health and wellness area.

• Impact through development of data analytics solutions that are meaningful and used in practice, impact through scientific co-operations and publications; (co-)author of over 120 articles in the field.
• Over 20 years experience in machine learning, statistics, signal processing, artificial intelligence methods.
• Leadership and management of multi-location team (>15 R&D professionals), co-ordination of large international multi-disciplinary R&D projects
• Communication of data analytics results and providing insights for different stakeholders
• Taking care of customer relationships
• Ph.D. in artificial intelligence/biomedical engineering, M.Sc. in applied physics
• Lecturing courses and guiding students and researchers


Healthcare is one of the most conservative fields in the uptake of new technologies. Reasons for this range from regulatory considerations to (informal and formal) processes that are difficult to change, but also technical issues, such as problems with the data and the difficulty of proving performance play a strong role. In this tutorial we will discuss issues we may run into when considering AI approaches for health applications. Subjects include (but are not limited to): how to get the input data right (poor quality data, missing data, harmonization), (lack of) Gold Standards and objective measures, black-box approaches vs. explainable models, data visualization, usability, classification performance vs cost-effectiveness vs practical meaningfulness. Examples of the issues and practical hints will be given based on real-life example cases of implemented systems. .... " 

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