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Big Data in Healthcare: Opportunities and Challenges
May 10, 2016 @ 5:30 pm - 9:30 pm
Opportunities and challenges of big data in healthcare.
This edition of the Hacking Health Café aims to bring together industry professionals and academic researchers to discuss cutting-edge innovation in healthcare possible with big data, open data and artificial intelligence.
Do not miss this opportunity to withness first-hand the stories and experiences from professionnals and to interact with them during the panel discussion!
How you can take advantages of big data in your organization? Participate in this Hacking Health Cafe on May 10th to discover all the opportunities and challenges of big data.
We are proud to partner with The Institute for data valorization (IVADO), the world’s most productive deep learning and operations research group.
Presentations of HH Café will take place at the amphitheater Justine Lacoste at the CHU Sainte-Justine.
Machine learning to improve in home healthcare
The aging of the population entails increasing health costs. A significant proportion of people aged 65 and over or develops multiple chronic conditions. Home care is an interesting alternative to traditional healthcare as they are less expensive and often more popular with patients. Machine learning can provide decision making assistance to nurses in prioritizing interventions with the aim of preventing hospital readmissions that are often expensive and avoidable. AlayaCare proposes an approach based on MaxOut neural networks for the prediction of unfortunate events. Preliminary results suggest that the method works better than alerts configured manually by nurses.
Jonathan Vallée, Data Science Director, AlayaCare – www.alayacare.com
Jonathan holds a M.Sc. in machine learning where he has been awarded the board of honors and has worked as a data science researcher before taking the leadership of JDA’s data science team and now AlayaCare’s. Jonathan is an inventor and co-inventor on four patents and strives to innovate the home healthcare industry
How data science helps create a big impact in healthcare services
As the field of personalized medicine is emerging we believe that their will be enormous challenges at the interaction between individual treatment planning and their execution, in the context of limited and expensive medical resources. In particular, we are going to focus our attention in areas where population aging is going to have a big impact, namely: cancer treatment, homecare services, and hospital logistics.
Louis-Martin Rousseau, Professor, Healthcare Analytics and Logistics, Polytechnique Montreal
Louis-Martin Rousseau is a full professor of Operations Research in the department of Mathematics and Industrial Engineering at École Polytechnique de Montréal, and since February 2016 he holds the Canada Research Chair in Healthcare Analytics and Logistics. His research focuses on in solving complex, integrated decision problems which appear both in supply chain planning and execution (and in healthcare. Louis-Martin was also the founder of Planora and Chief Science Officer of Planora for 10 years that, before its acquisition by JDA in 2012, propose a scheduling SaaS solution to the heath sector.
Open Data for the acceleration of knowledge
CARTaGENE (CaG) is both a population-based biobank and the largest ongoing prospective health study of men and women in Quebec. It is a genetic epidemiological infrastructure consisting of a large databank and biobank, with regulatory oversight and governance. The program recruited 43 000 individuals, aged 40-69 years, representing 6 metropolitan regions of the province and collected detailed lifestyle, health and medical data on these individuals. The program also gathered detailed physical measurements, clinical and biochemical measures at baseline. The fundamental goal of this unique public-funded project is to support the scientific community in identifying the determinants of chronic diseases of environmental and/or genetic origin. It was also created to accelerate the process of translational medicine through the identification of biomarkers for early diagnosis, disease treatment and prevention. CaG has been offering open access to data and biosamples since 2010 and more than 30 projects have already been granted access. These ongoing partnerships, our interoperability with other national biobanks, and our plan to develop other sectors of activity support our mission to provide meaningful high-quality data and biospecimens for years to come.
Joseph Tcherkezian, Senior Research Associate, Cartagene – www.cartagene.qc.ca
Joseph is in charge of Data Access and Business Development at CARTaGENE and interacts extensively with potential cohort users, promotes the resource externally and is involved in strategic funding initiatives. Joseph obtained his Ph.D. from McGill University in molecular biology. He then moved to Boston to pursue his postdoctoral training in neurobiology at Harvard Medical School. In 2010, He returned to Montréal and worked as a research associate at the Institute for Research in Immunology and Cancer (IRIC). In 2014 he joined the Laboratory for Therapeutic Development at McGill University where he planned and executed preclinical proof-of-concept studies for antineoplastic drug candidates.
Data sharing in the field of cancer genomics
Data is at the center of academic research in life sciences and diligent inspection already delivers tangible benefits that will impact clinical practices. Despite a ubiquitous desire for universal access to high-quality datasets, generation and sharing of data is still perceived as an inconvenient prerequisite (or a necessary duty) in the process of securing research funds and publishing scientific results. I will illustrate this data sharing paradox using concrete examples from the field of cancer genomics.
Sébastien Lemieux, Researcher, IRIC (Institute for Research in Immunology) – www.iric.ca
Following a Ph.D. in Computer Sciences and a post-doc in the biotech industry (Elitra, now Merck & Co), Sébastien Lemieux has been leading, since 2005, a bioinformatics research lab at IRIC. His research aims at developing approaches to take advantage of data-intensive molecular characterizations (proteomics, transcriptomics and drug screening) of patient samples to assist in clinical decisions and provide better treatments. His main application area is in adult leukemia.
Artificial Intelligence for medical image analysis
Nicolas Chapados, Chief Science Officer, Imagia – www.imagia.com
Nicolas Chapados holds an engineering degree from McGill University and a PhD in Computer Science from University of Montreal, Canada. While still writing his thesis and jointly with his advisor Yoshua Bengio, he co-founded ApSTAT Technologies in 2001, a machine learning technology transfer firm, to apply cutting-edge academic research ideas to areas such as biomedical research, supply chain planning, business forecasting, and hedge fund management. He also co-founded two spin-off companies: Imagia, to automate the diagnosis of cancer tumors from medical images using deep learning, and Chapados Couture Capital, a Quebec-registered quantitative asset manager. Previously, Nicolas was a member of speech recognition research group at Nortel Networks, where he led the research and implementation of a natural-language dialog manager for continuous speech recognition applications.