General Chairs

Alex MH Kuo, PhD

Professor, School of Health Information Science, University of Victoria, Canada.
Chair, Special Interest Group on Big Data for Healthcare, Medicine and Biology, IEEE TCBD.

Elizabeth Borycki, RN PhD FACMI, FCAHS, FIAHSI

Professor, School of Health Information Science
Director, Global Laboratory for Digital Health Innovation, University of Victoria, Canada

Technical Program Committee

Alex Thomo Professor  University of Victoria, Canada.
Yue-Shan Chang Professor National Central University, Taiwan
Shu-Lin Wang Associate Professor National Taichung University of Science and Technology, Taiwan.
Robert Hsu Professor National Chung Cheng University, Taiwan.
Elizabeth Borycki Professor University of Victoria, Canada.
Andre Kushniruk Professor University of Victoria, Canada.
Jia-Lin Liu Professor West China Hospital Sichuan University, Sichuan, China. 
Dillon Chrimes Assistant Teaching Professor University of Victoria, Canada.
Hao Qi Chief Physician 1st Hospital of Shanxi Med. Univ. Taiyuan, Shanxi  Province, P. R. China.
Kenneth Moselle Director Applied Clinical Research Unit, Island Health, BC, Canada.
Bakhtiar Amen Lecturer Computer Science Department Centre of Artificial Intelligence University of Huddersfield, UK.
Hashim Abu-gellban Ph.D Texas Tech University, USA.


Big Data is a collection of data so large, so complex, so distributed, and growing so fast (or 5Vs- volume, variety, velocity, veracity, and value). It has been known for unlocking new sources of economic value, providing fresh insights into sciences, and assisting on policy making. Healthcare and life science is the most data intensive industry in the world. Huge volumes of very heterogeneous raw data are generated daily by a variety of modern clinical information systems, such as Electronic Health Records (EHRs), Computerized Physician Order Entry (CPOE), Laboratory Information Systems, and Picture Archiving and Communications System (PACS), Medical sensors can generate unimaginable volumes of patient data, per year. These information systems are utilized for functionalities in many healthcare settings such as physician offices and hospitals. Several published studies have asserted that Big Data managed efficiently can improve care delivery while reducing healthcare costs. A number of real world practices and cases also reported using Big Data to improve Healthcare, Life Science, and better Health Policy Decision Making.

Health Big Data Analytics Challenges

Extracting useful healthcare knowledge from Big Data can be considered as a processing pipeline that involves multiple distinct configuration stages to achieve full utilization. Each stage faces several specific challenges as follows:

  1. Data aggregation challenges:

Big Data research projects usually involve multiple organizations, different geographic locations and large numbers of researchers. Therefore, data exchange between groups is very difficult when using this method. In addition,  the need to ensure patient data security, confidentiality and privacy based on mandated privacy to the general public by the privacy commissioner. There are many barriers to health Big Dara aggregation. With large datasets, it is all too easy to unveil significant value by making information transparent. Thus, our ability to protect individual privacy in the era of Big Data is limited.

  1. Data maintenance challenges:

Since Big Data involves large collections of datasets, it is very difficult to efficiently store and maintain the data in a single hard drive using traditional data management systems such as relational databases. Also, it is a heavy IT burden (cost and time) for small organizations or labs to manage.

  1. Data integration challenges:

This involves integrating and transforming data into an appropriate format for subsequent data analysis. However, Big Data in healthcare are unbelievably large, distributed, unstructured and heterogeneous, making integration and transformation all the more problematic. Integrating unstructured data is a major challenge for BDA. With structured EHR data integration there are also many integration issues.

  1. Data analytic challenges:
  • Complexity of the analysis – For some analysis algorithms, the computing time increases dramatically even with small amounts of data growth. For example, Bayesian Network is a popular algorithm for modeling knowledge in computational biology and bioinformatics. However, within the computation complexity of the Bayesian Network, the computing time for finding the best network also increases exponentially as the number of records rises.
  • Parallelization of computing model – For those computationally intense problems, we can parallelize the analysis so that the problem can be solved by distributing tasks over many computers. However, if we cannot parallelize the analytic algorithm, it will be very difficult for those massive parallel-processing (MPP) tools to perform an efficient computation.
  1. Pattern interpretation/application challenges:

Having the ability to analyze Big Data is limited in its value if decision makers cannot understand the discovered patterns. Unfortunately, due to the complex nature of the analytics in healthcare, presentation of the results, data visualization, and its interpretation by non-technical domain experts are a major challenge.

Objectives & Research Topics

The main objective of this multidisciplinary workshop was to gather both researchers and practitioners to discuss technical and non-technical challenges, and explore previously unknown challenges and resolutions in the Big Data Analytics process for Healthcare, Medicine, and Biology. Papers describing original research on both theoretical and practical aspects of Healthcare Big Data Analytics (BDA) are solicited. Topics include, but are not limited to:

  • Big Data Analytics Infrastructure, methodologies and tools for Healthcare Big Data Analytics (BDA)
  • Big Data for Precision/Customized Medicine
  • Health Big Data Privacy/Security
  • Health Big Data Management/Repositories
  • Standard Development for Healthcare Big Data Governance/Interoperability
  • Metadata for Healthcare Big Data Integration, Discovery and Interpretation
  • Machine Learning/Deep Learning for Big Data Analytics in Biology, Medicine, and Healthcare
  • Visualization Analytics for Big Data in Biology, Medicine, and Healthcare
  • Real World Big Data Analytics Case Study in Biology, Medicine, and Healthcare
  • Availability, reliability and Fault tolerance
  • Frameworks for parallel and distributed information retrieval
  • Data-driven decision making and prescriptive analytics
  • High-dimensional data streams for BDA