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WCUC 2020 - Practical Solutions for Complex Problems

Join us in beautiful Vancouver, British Columbia for the second Western Canadian Universities Big Data Health Conference (WCUC 2020) as we explore opportunities to spark collaborations, address common challenges, and identify actionable steps for using large-scale data analysis and technology to improve human health and healthcare.


Conference topics will include:

+ Precision Health

+ Visualization

+ Distributed Analytics

+ Novel Data Sources (eg: real-time data)

+ Bioinformatics and Computational Biology

+ Knowledge Translation

 

            WHEN                                 WHERE 

                       TBA                                              Wosk Centre for Dialogue

                                                                                  580 W Hastings St,

                                                                       Vancouver, BC V6B 5K3 Canada   

               REGISTRATION INFORMATION

                                Registration Fee: $150.00 + service fees

                   

 


Agenda


This event is taking place at the Morris J. Wosk Centre for Dialogue in Vancouver British Columbia

Day 1 

8:00 – 8:45 am | Registration + Continental Breakfast

8:45 – 9:00 am | Welcome and Introductions

9:00 – 9:45 am | Keynote Address – Dr. Douglas Kingsford, BC Ministry of Health – CMIO and lead for Digital Health Strategy

9:45 – 10:00 am | BREAK

10:00 – 12:30 pm | Concurrent Workshop/Training Sessions (more information below)

12:30 pm – 2:00 pm | Lunch and Keynote Address - Corinne Hohl, Associate Professor in the Department of Emergency Medicine at UBC and Scientist at the Centre for Clinical Epidemiology and Evaluation

2:00 – 4:00 pm | Rapid-fire Presentations and Q and A Sessions

4:00 – 5:30 pm | Poster and Networking Session

End of Day 1 

Day 2

8:00 – 9:00 am | Registration + Continental Breakfast

9:00– 9:45 am | Keynote Address – Dr. Jessica Dennis, Assistant Professor in the Department of Medical Genetics at UBC and an Investigator at the BC Children’s Hospital Research Institute

9:45 – 10:00 am | BREAK

10:00 – 12:30 pm | Concurrent Workshops/Training Sessions (more information below)

12:30 – 1:45 pm | Lunch

1:45 pm – 2:00 pm | Closing Remarks

*Some rooms will be available for groups who wish to continue working informally for the duration of the afternoon.

End of Day 2


Keynote Speakers

Corrine Hohl

Corinne Hohl

Associate Professor in the Department of Emergency Medicine at UBC and Scientist at the Centre for Clinical Epidemiology and Evaluation - "ActionADE: A joint venture to improve patient safety"

Dr. Douglas Kingsford

Dr. Douglas Kingsford

BC Ministry of Health - Chief Medical Information Officer and lead for Digital Health Strategy -- "BC's Digital Health Initiative"

Dr. Jessica Dennis

Dr. Jessica Dennis

Assistant Professor in the Department of Medical Genetics at UBC and an Investigator at the BC Children's HospitalResearch Institute -- “No health without mental health: Harnessing electronic health records linked to a DNA biobank to understand relationships between mental and physical health”


Workshops


* Offered on both days of the conference

Day 1

Analyses of health data often involve large 2 tables and complex relationships among variables. While R is an increasingly popular tool for working with and analyzing health data, larger datasets can present significant challenges.  In this workshop, attendees will explore practical strategies for analyzing large datasets in R, with code that is both easier to write and results in more efficient processing.

  1. Presenter

    Robert Balshaw PhD, Senior Statistician, George & Fay Yee Centre for Healthcare Innovation, University of Manitoba

    Robert Balshaw is a consulting biostatistician with 25 years of experience in government, academia, and the pharmaceutical industry, using R (and S before that) throughout that time.  He has worked on hundreds of projects involving data sets ranging in size from less than a kilobyte to 10s of gigabytes and he loves sharing what he has learned about statistical programming and analysis.
     

     

This is an introductory workshop on Machine Learning for beginners. In this workshop, we will discuss what AI and machine learning are, how they evolved, and how they work. We will review some examples and applications of these methods and introduce resources for becoming hands-on with machine learning. By the end of the workshop, the trainees will learn the following:

  • Introduction to artificial intelligence (AI)
  • Introduction to machine learning
  • AI and machine learning in healthcare
  1. Presenters

    Delaram Behnami, M.A.Sc., University of British Columbia

    Mohammad Jafari, M.A.Sc., University of British Columbia

    Both Delaram and Mohammad are PhD Candidates in Electrical and Computer Engineering, and both have experience in teaching machine learning to both undergraduate and graduate trainees (e.g. machine learning for undergraduate engineering students, workshop on deep learning for graduate engineering students, co-instructors at the UBC ECE course EECE-571T: Advanced Machine Learning Tools for Engineers).

This workshop will teach attendees how to utilize various NLP models and tools to analyze health data, particularly in free-form texts. This session will be taught by a multidisciplinary team composed of a senior professor, two assistant professors, and a machine-learning engineer, and will expose participants to multiple topics:

  • The big picture of the field of medical informatics
  • A history and state-of-the-art of NLP
  • Concrete projects in which NLP has played crucial roles
  • Detailed techniques on how to build up NLP tools from scratch by utilizing existing packages.
  1. Dr. Quan Long

    Dr. Quan Long graduated from Peking University with a PhD in Applied Mathematics (majoring in software engineering). After graduation, he worked in IBM Research for a year; and then entered the exciting biological world, starting by serving for path-finding international genomics projects. Currently Dr. Long is leading a small group to develop bioinformatics and biostatistics tools for application on medical and biological problems. He develops novel machine/statistical learning approaches, tailoring to large data such as -omics and electronic health records (EHR). Dr. Long has published in a number of leading journals, including Nature, Science, Nature Genetics, Bioinformatics, PLoS Comp Biol, and Genetic Epidemiology, attracting 20,000 citations (Google Scholar). He also served as a reviewer for established journals including Bioinformatics, Briefings in Bioinformatics, Cell, Molecular Biology & Evolution, Nature Biotechnology, Nature Communications, Nucleic Acids Research, PLoS Comp Biol, Science, Trends in Genetics, and various journals in the field of statistics.

  2. Dr. Maria J. Santana

    Dr. Santana is a health services researcher, patient and family-centred care scientist, and an Assistant Professor in the departments of Pediatrics and Community Health Sciences at the University of Calgary. She is the lead, Alberta Strategy for Patient-Oriented Research (SPOR), Patient Engagement. Dr. Santana is also the co-director of The Methods Hub at the O’Brien Institute for Public Health. She has received training in clinical pharmacy, public health, and clinical epidemiology. Her research focuses in developing novel methods to integrate the voice of patients and family caregivers in health care and health service research to improve health and health care. The methods advance person-centred care and patient-oriented research.

  3. Dr. Hude Quan

    Dr. Hude Quan, a Professor in the department of Community Health Sciences at the University of Calgary, is an internationally recognized data scientist and methodologist developing methods to optimize the use of data for research and healthcare system performance evaluation. He directs the following: 1) WHO Collaborating Centre for Classification, Measurement and Standardization to improve the ICD coding system; 2) Methods Support & Development Platform for the Alberta Strategy for Patient Oriented Research to support research endeavours; and 3) Centre for Health Informatics, to advance precision medicine and public health through innovating data science technology. His methodology-based research and cross-sectoral collaboration have produced translational knowledge that has made significant worldwide changes to research practice regarding the use of data.

  4. Dr. Zilong Zhang

    Dr. Zilong Zhang (Todd) is a research associate at the University of Calgary. His primary focus is to
    develop and validate natural language processing models to mine information from free text in the
    healthcare field. Dr. Zhang has completed two projects using NLP techniques to analyze patients concerns and the auto-detection of pressure ulcer in discharge summaries. He has also has helped his colleagues with issues related to deep learning and machine learning. Before being a research associate, Dr. Zhang spent four years as an audio signal and speech recognition engineer in Shanghai.

Day 2

This workshop aims to introduce machine learning to beginners with minimal experience. Attendees will receive a high-level description of machine learning, along with a demonstration of the steps involved and how they serve to mitigate bias. Topics include:

  • Separating data into a test and training set, variable (“feature”) selection
  • Over-fitting data
  • 10- fold cross validation.

Additionally, a case study using CPCSSN data will also be used in the workshop. Attendees will explore
CPCSSN, the data it holds, and current machine learning initiatives aimed at data quality improvement. A study which used machine learning to construct a case definition for diabetes mellitus will be described; results from a traditional study and the machine learning approach will be compared.
 

  1. Presenters

    Dr. Tyler Williamson is an Associate Professor of Biostatistics at the University of Calgary. He is the Associate Director of the Centre for Health Informatics and the Co-Director of the Health Data Science Program at University of Calgary. Dr. Williamson is an internationally recognized expert in applications of machine learning in primary care EMR data.


    Larka Soos is a PhD student specializing in biostatistics in the Department of Community Health Science at the University of Calgary. Her research involves the application of machine learning methods in linked primary care electronic medical record (EMR) and hospital administrative data. Larka is also a data manager in the Southern Alberta node of the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) since 2015.


    Matt Taylor is a health data scientist and data manager with the Southern Alberta Primary Care
    Research Network (SAPCReN), which is the Southern Alberta node of CPCSSN. Matt is an expert in software engineering and extracting and processing primary care EMR data, and leads a project aiming to clean EMR medication information using machine learning.

This workshop will focus on learning observational data analysis approaches in a healthcare data analysis context, particularly aimed at:

  • Demonstrating the implementation of propensity score analysis in a real-world data analysis
  • context through a hands-on data analysis exercise
  • Explaining how these analyses are different than conventional regression methods
  • Understanding assumptions/diagnostics of these models.

The workshop will: (i) describe the basic concepts; (ii) give specific software instructions (in R), with a live demonstration of an analysis with a real dataset; (iii) include a general discussion of the best practices and guidelines for applying these methods; and (iv) cover some of the advanced topics – e.g., the ‘high-dimensional propensity score’ algorithm, explaining the rationale, use/applications, and potential enhancements of this method with machine learning methods/interesting data dimensions in
the large healthcare analysis context.

  1. Presenters

    Dr. Ehsan Karim is an Assistant Professor at the UBC School of Population and Public Health, and a Scientist at the Centre for Health Evaluation and Outcome Sciences, St. Paul's Hospital. He obtained his PhD in Statistics from UBC, and completed his postgraduate training in the Department of Epidemiology/Biostatistics at McGill University, Montreal. His current program of research focuses on developing causal inference methodologies and applications of data science approaches in the large healthcare data context in answering real-world questions. He is supported by the Michael Smith Foundation for Health Research Scholar award, grants from NSERC, and BC SUPPORT Unit.

This workshop will teach attendees how to utilize various NLP models and tools to analyze health data, particularly in free-form texts. This session will be taught by a multidisciplinary team composed of a senior professor, two assistant professors, and a machine-learning engineer, and will expose participants to multiple topics:

  • The big picture of the field of medical informatics
  • A history and state-of-the-art of NLP
  • Concrete projects in which NLP has played crucial roles
  • Detailed techniques on how to build up NLP tools from scratch by utilizing existing packages.
  1. Dr. Quan Long

    Dr. Quan Long graduated from Peking University with a PhD in Applied Mathematics (majoring in software engineering). After graduation, he worked in IBM Research for a year; and then entered the exciting biological world, starting by serving for path-finding international genomics projects. Currently Dr. Long is leading a small group to develop bioinformatics and biostatistics tools for application on medical and biological problems. He develops novel machine/statistical learning approaches, tailoring to large data such as -omics and electronic health records (EHR). Dr. Long has published in a number of leading journals, including Nature, Science, Nature Genetics, Bioinformatics, PLoS Comp Biol, and Genetic Epidemiology, attracting 20,000 citations (Google Scholar). He also served as a reviewer for established journals including Bioinformatics, Briefings in Bioinformatics, Cell, Molecular Biology & Evolution, Nature Biotechnology, Nature Communications, Nucleic Acids Research, PLoS Comp Biol, Science, Trends in Genetics, and various journals in the field of statistics.

  2. Dr. Maria J. Santana

    Dr. Santana is a health services researcher, patient and family-centred care scientist, and an Assistant Professor in the departments of Pediatrics and Community Health Sciences at the University of Calgary. She is the lead, Alberta Strategy for Patient-Oriented Research (SPOR), Patient Engagement. Dr. Santana is also the co-director of The Methods Hub at the O’Brien Institute for Public Health. She has received training in clinical pharmacy, public health, and clinical epidemiology. Her research focuses in developing novel methods to integrate the voice of patients and family caregivers in health care and health service research to improve health and health care. The methods advance person-centred care and patient-oriented research.

  3. Dr. Hude Quan

    Dr. Hude Quan, a Professor in the department of Community Health Sciences at the University of Calgary, is an internationally recognized data scientist and methodologist developing methods to optimize the use of data for research and healthcare system performance evaluation. He directs the following: 1) WHO Collaborating Centre for Classification, Measurement and Standardization to improve the ICD coding system; 2) Methods Support & Development Platform for the Alberta Strategy for Patient Oriented Research to support research endeavours; and 3) Centre for Health Informatics, to advance precision medicine and public health through innovating data science technology. His methodology-based research and cross-sectoral collaboration have produced translational knowledge that has made significant worldwide changes to research practice regarding the use of data.

  4. Dr. Zilong Zhang

    Dr. Zilong Zhang (Todd) is a research associate at the University of Calgary. His primary focus is to
    develop and validate natural language processing models to mine information from free text in the
    healthcare field. Dr. Zhang has completed two projects using NLP techniques to analyze patients concerns and the auto-detection of pressure ulcer in discharge summaries. He has also has helped his colleagues with issues related to deep learning and machine learning. Before being a research associate, Dr. Zhang spent four years as an audio signal and speech recognition engineer in Shanghai.