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Medial EarlySign to Present at Leading Healthcare Events on Combining AI with Existing Medical Data to Identify Individuals at Highest Risk of Disease

Presentations Will Discuss Application of Machine Learning and Big Data to Improve Population Health


KFAR MALAL, Israel – May 16, 2017 – Medial EarlySign (, a developer of machine learning tools for data driven medicine, today announced that company executives will be presenting at two upcoming leading industry events in Boston, Massachusetts: the Medical Informatics World Conference and the Third Global Deep Learning in Healthcare Summit. Both events will take place next week at the Renaissance Waterford Hotel in Boston.


“Machine learning and big data are empowering physicians and providers by providing tools and insight that can facilitate personalized, predictive and proactive outreach and identify high-risk patients, helping to improve treatment and outcomes,” said Ori Geva, Co-founder and CEO, Medial EarlySign. “We are excited to be part of the global discussion and to share our vision and results with the wider medical technology and population healthcare communities.”


In his presentation Leveraging “Ordinary” EMR Data with AI Today to Identify Tomorrow’s “High Risk” Population, Dr. David Yavin, President, North America for Medial EarlySign, will discuss how applying AI and market-proven financial algorithms to standard electronic medical records can identify individuals at highest risk of harboring cancers and other life-threatening diseases, ultimately saving more lives and enabling healthcare providers to prioritize and allocate resources more effectively.

Dr. Yavin’s presentation will take place at the Fifth Annual Medical Informatics World Conference on Monday, May 22, 2017 at 12:30pm.


Avi Shoshan, Head of Data Science Framework for Medial EarlySign, will present A Journey into Learning from Medical Records, explaining the primary challenges in working with large datasets of patient records from different sources, a pipeline of tools built to handle those problems, and actual modeled examples built with it. Problems such as efficient ways to hold the data, cleaning, normalizing, complex feature generators, modeling, data bias, and performance testing will be discussed combined with real-life examples from databases of millions of patient records.

The presentation will take place at the Third Global Deep Learning in Healthcare Summit on Thursday, May 25, 2017 at 3:40pm.


Medial EarlySign’s algorithmic framework enables the processing of hundreds of millions of clinical data points, collected from basic medical information such as blood test results and other EMR data, to create solutions that scan populations or individuals and identifies those at high risk of harboring numerous diseases. In February, the company announced first-year results of its implementation with Maccabi Healthcare Services (MHS), for ColonFlag, a tool developed in collaboration with MHS to identify individuals with a high probability of having colorectal cancer.


Medial EarlySign is working on several potential clinical outcomes and conducting clinical studies with more than five million patients in 14 institutions around the world.