Participation in panel session on evidence synthesis at ICHI 2016


Prof Sophia Ananiadou, Director of NaCTeM, will act as a panelist in a a special session entitled Evidence Synthesis – Current Practices and Future Possibilities to be held on October 5th, 2016, as part of the IEEE International Conference on Healthcare Informatics (ICHI 2016), to be held in Chicago, IL, USA.

Panel Chair

Neil R. Smalheiser, University of Illinois at Chicago, College of Medicine


Spyros Kitsiou, University of Illinois at Chicago, College of Medicine
Aaron M. Cohen, Oregon Health & Science University
Siddhartha Jonnalagadda, Microsoft
Byron Wallace, Northeastern University
Sophia Ananiadou, The University of Manchester, National Centre for Text Mining


This session has two main goals. First, it is designed to inform new investigators of the importance of automating evidence synthesis, and emphasize the potential for new research in this area. Second, it provides an opportunity for five of the leading laboratories across the US and UK to come together, to review the current state of the art, and discuss in detail the nuts-and-bolts of different technical approaches to overcoming the key challenges of evidence synthesis.

Dr. Kitsiou will give an overview of the different types of literature reviews and evidence synthesis approaches in health informatics, with emphasis on systematic reviews and meta-analyses, for those not in the field: discussing how they are generated, and the bottlenecks in generating them efficiently and with adequate quality. Dr. Cohen will give an overview of the efforts by laboratories worldwide to improve and automate the process of writing and updating systematic reviews in evidence-based medicine. Dr. Smalheiser will present his research in identifying relevant clinical trials to examine, whereas Drs. Jonnalagadda, Wallace, and Ananiadou will discuss and compare their approaches to extracting data from clinical trial articles. Finally, there will be guided general discussion to consider the scope, limitations and potential for text mining techniques to automate, streamline and re-engineer the largely manual process of writing systematic reviews.

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