Special Session
Special Session on
Machine Learning and Deep Learning Improve Preventive and Personalized Healthcare -
Cognitive Health IT
2020
24 - 26 February, 2020 - Valletta, Malta
Within the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSTEC 2020
* CANCELLED *
CO-CHAIRS
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Tahir Hameed
Merrimack College
United States
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Brief Bio
Dr. Tahir Hameed is an Assistant Professor of Management at Merrimack College, North Andover, MA. He specializes in information systems, technology management and business analytics areas. Dr. Hameed obtained his PhD in Information Technology Management in 2012 from the Korea Advanced Institute of Science and Technology (KAIST). He also earned a master’s degree in computer science from LUMS in 2001 and a bachelor’s degree in industrial electronics engineering from NED University of Engineering & Technology in 1995. Before starting in academia, he worked for a decade in industrial automation, software and IT sectors. From 2012 to 2018, Dr. Hameed served as Assistant and Associate Professor at SolBridge International School of Business in South Korea. His research primarily focuses on technology innovation management, online health information, and healthcare analytics. In the area of technology innovation management, his research on technology standardization and commercialization in catching-up nations has been published in journals such as Technology Analysis and Strategic Management, Technological Forecasting and Social Change, World Development, Telecommunications Policy, and Sustainability. In the field of online health information and healthcare analytics, his research focuses on theoretical and practical issues in online health information and clinical decision support systems, with recent publications in Information Sciences and Computers in Human Behavior. He can be reached at hameedt@merrimack.edu.
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Syed Ahmad Chan Bukhari
St. John's University
United States
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Brief Bio
Dr. Bukhari is an Assistant Professor and Director of Healthcare Informatics at St. John's University, New York. He received his Ph.D. in Computer Science from the University of New Brunswick, Canada, and completed his postdoctoral fellowship at Yale University. He was also a core research team member at CEDAR Metadata Center, Stanford University. With the Stanford team, he studied scientific experimental reproducibility and completed several projects to improve biomedical data FAIRness and reproducibility. Dr. Bukhari received NIH (ORISE) NCBI fellowship in 2016 to work at National Center for Biotechnology Information (NCBI), Bethesda. Dr. Bukhari developed protocols and pipelines (MiAIRR, CAIRR, and CEDAR-to-NCBI ) for the standardized authoring, validating, and submitting scientific data to the NCBI repositories with NCBI Scientists. His developed resources are now considered de facto resources for several biomedical communities such as AIRR-C & Antibody society. At St. John's University, he teaches healthcare Informatics and computer science to graduate and undergrad students. His current research efforts focus on addressing several core problems in healthcare informatics and data science. Dr. Bukhari has received multiple internal and external grants to support his research efforts.
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SCOPE
Machine learning has transformed healthcare by improving disease prediction, diagnosis, prognosis, and treatments. Using large but relatively structured datasets like electronic health records (EHRs), scans, and labs, they provide indispensable tools and decision support to healthcare providers and patients. Lately, with bigger, more complex and unstructured datasets available, healthcare apps and clinical decision support systems (CDSS) have started to leverage deep learning to refine these recommendations. Such systems not only have prediction but learning capabilities also. Consequently, they enable preventive and rehabilitative healthcare that is highly personalized and adaptive. This session seeks completed research on applications of deep learning and cognitive computing in preventive care, personalized treatments and adaptive CDSS aiming to better health outcomes, patient satisfaction and costs.
Topics of Interest
Topics of interest include, but are not limited to:
- Machine Learning
- Deep learning and Cognitive Computing approaches in Disease Prediction
- Diagnosis
- Prognosis
- Personalized Treatments
- Clinical Decision Support Systems (CDSS)
- Adaptive CDSS
- Precision Medicine and more to enhance preventive and adaptive healthcare
IMPORTANT DATES
Paper Submission:
December 28, 2019 (expired)
Authors Notification:
January 9, 2020 (expired)
Camera Ready and Registration:
January 17, 2020 (expired)
SPECIAL SESSION PROGRAM COMMITTEE
Qasim Bukhari,
Massachusetts Institute of Technology, United States
Safee Ullah Chauhdry,
Lahore University of Management Sciences, Pakistan
Hasan Ali Khattak,
National University of Sciences and Technology (NUST), Pakistan
Ikram Ullah Lali,
University of Gujrat, Pakistan
Syed Qasim Bukhari,
Massachusetts Institute of Technology, United States
Rana Zia Ur Ur Ur Rehman,
Newcastle University, United Kingdom
Amnah Siddiqa,
Emory University School of Medicine, United States
Bobby Swar,
Concordia University of Edmonton, Canada
PAPER SUBMISSION
Prospective authors are invited to submit papers in any of the topics listed above.
Instructions for preparing the manuscript (in Word and Latex formats) are available at: Paper Templates
Please also check the Guidelines.
Papers must be submitted electronically via the web-based submission system using the appropriated button on this page.
PUBLICATIONS
After thorough reviewing by the special session program committee, all accepted papers will be published in a special section of the conference proceedings book - under an ISBN reference and on digital support - and submitted for indexation by DBLP, Web of Science / Conference Proceedings Citation Index, EI, SCOPUS, Microsoft Academic, Semantic Scholar and Google Scholar.
SCITEPRESS is a member of CrossRef (http://www.crossref.org/) and every paper is given a DOI (Digital Object Identifier).
All papers presented at the conference venue will be available at the SCITEPRESS Digital Library