Big Data Analytics for Health and Medicine (BDA4HM 2022)
A Workshop at 2022 IEEE International Conference on Big Data (IEEE Big Data 2022)
December 17th - 20th, 2022, Osaka, Japan
Just as Big Data has revolutionised the way data is managed across industries, it has begun to make significant changes in Health and Medicine, leading to reductions in costs of treatment, improved diagnosis, predicting outbreaks, prevention of disease, and improving the quality of life. For instance, 94% of the hospitals in the US have adopted the use of Electronic Health Records, which according to a McKinsey report, has “improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests”. Using Big Data Analytics in Health and Medicine can further optimise the process a patient goes through, further democratise access to specialised healthcare and reduce costs.
This workshop will focus on the cutting-edge developments from both academia and industry, with a particular emphasis on novel techniques to capture, store and process big data from a wide range of sources to improve health and medicine, and in particular on the methodologies and technologies which can be applied to correlate, learn and mine, interpret and visualise data which will improve health and medical processes.
This workshop is timely and interesting for researchers, academics and practitioners in big data processing and analytics and health and medicine. The workshop is very relevant to the big data community, especially data mining, machine learning, cyber-physical systems, and computational intelligence. It will bring forth a lively forum on this exciting and challenging area at the conference.
The workshop only considers well-written manuscripts that describe original, unpublished, state-of-the-art research and practical work. Indicative topics for the workshop are as follows:
Big Data for Health and Medicine
- Big data analytics for health and medicine
- Data mining and machine learning for health and medicine
- Clinical decision support systems
- Big data analytics for medical cost reduction
- Reduction of resource requirements for healthcare processes
- Big data for improved staffing and personnel management
- Managing healthcare data
- Big data and electronic health records
- Big data analytics for prediction and improvement of patient engagement
- Predicting health risks
- Big data and machine learning for disease prediction and prevention
- Informed strategic health planning
- Epidemic and Pandemic management
- Optimised resource allocation
- Big data for drug discoveries
- Ensuring healthcare data compatibility
- Health data provenance and confidentiality
- Bias detection, prevention and mitigation
- Big data for detecting and preventing fraud in health and medicine
- Big data for reducing unnecessary emergency visits
- Big data for Advanced Risk & Disease Management
- Data security and protection for health records
- Improving patient outcome/risk factors through Big Data
- Ethical implications of Big Data and Analytics in Health and Medicine
Papers following similar themes that fall within the broader domain of Big Data Analytics for Health and Medicine will also fit within the workshop’s scope.
To contribute toward advances in knowledge, the workshop will solicit submissions of manuscripts from researchers and practitioners who are actively working in Big Data Analytics for Health and Medicine.
Papers should be formatted using the two column IEEE CS template and can be up to 10 pages (including references) in length using page size of 8.5” x 11”.
The submission system will be made available shortly.
Each submission will be peer reviewed by at least 2 peers.
Please note that the authors of each submitted paper will be expected to review one other paper.
Important Dates (All dates now firm)
|Oct 1, 2022||Due date for full workshop papers submission|
|Nov 1, 2022||Notification of paper acceptance to authors|
|Nov 20,2022||Camera-ready of accepted papers|
|Dec 17-20 2022||Workshop (one day of)|
Workshop Program Co-Chairs
Dr Stephen McGough
Reader in Machine Learning
School of Computing Science
E-mail : email@example.com
Dr Matthew Forshaw
Reader in Data Science
School of Computing
Dr Amir Atapour Abarghouei
Department of Computing Science
Durham, DH1 3LE
International Technical Committee
To be confirmed
|Xiaoxuan Liu||University of Birmingham, UK|
|Alisha Davies||London School of Hygiene and Tropical Medicine, UK|
|Kate Farrahi||University of Southampton, UK|
|Avi Goldfarb||University of Toronto, Canada|
|Marzyeh Ghassemi||MIT, USA|
|Vanessa Gómez Verdejo||Universidad Carlos III de Madrid, Spain|