Big Data Analytics for Health and Medicine (BDA4HM 2023)

A Workshop at 2023 IEEE International Conference on Big Data (IEEE Big Data 2023)

December 15th - 18th, 2023, Sorrento, Italy

## Final Programme

Time Title Authors
14:00 - 14:05 Welcome Chairs
14:05 - 14:25 Data Poisoning Attacks over Diabetic Retinopathy Images Classification Marcello Di Giammarco, Francesco Mercaldo, Antonella Santone, and Fabio Martinelli
14:25 - 14:45 Towards automatic identification of self-reported COVID-19 tweets: Introducing a multilingual manually annotated dataset, baseline systems and exploratory evaluations Ramya Tekumalla, Luis Alberto Robles Hernandez, and Juan Banda
14:45 - 15:05 A Novel Ensemble-Based Framework for Feature Identification and Classification of COVID-19 Electronic Health Record Data Asmita Mahajan and Durga Toshniwal
15:05 - 15:25 Forecasting COVID-19 Hotspots in Florida Public Schools: A Machine Learning Approach for Informed Decision-Making Mingming Peng, Hongmei Chi, and Askal Ali
15:25 - 15:45 Enhancing Vocal-Based Laryngeal Cancer Screening with Additional Patient Information and Voice Signal Embedding Jaemin Song, Yong Oh Lee, Seho Park, Youn Kyu Lee, Hansang Park, and Hyun- Bum Kim
15:45 - 16:05 Recent Artificial Intelligence Advances in Detection and Diagnosis of Sickle Cell Disease: A review Abdourahmane Balde, Avewe Bassene, Lamine Faty, Mamadou Soumboundou, Ousmane Sall, and Youssou Faye
16:05 - 16:20 Coffee Break  
16:20 - 16:40 Exploring the Value of Pre-trained Language Models for Clinical Named Entity Recognition Samuel Belkadi, Lifeng Han, Yuping Wu, and Goran Nenadic
16:40 - 17:00 Interpretable and robust hospital readmission predictions from Electronic Health Records Hugo Calero, Rebeen Hamad, Christian Atallah, John Casement, Dexter Canoy, Nick Reynolds, Michael Barnes, Paolo Missier, and The AI-MULTIPLY Consortium
17:00 - 17:20 NCL-SM: A Fully Annotated Dataset of Images from Human Skeletal Muscle Biopsies Atif Khan, Conor Lawless, Amy Vincent, Charlotte Warren, Valeria Di Leo, Tiago Gomes, and A. Stephen McGough
17:20 - 17:40 How much data do I need? A case study on medical data Ayse Betul Cengiz and Andrew Stephen McGough
17:40 - 18:00 Graph Encoding-Enhanced Transformer for Drug Recommendation Xunsheng Cai, Syauki Thamrin, and Arbee Chen
18:00 - 18:10 Closing Remarks  

Outline

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.

Previous workshops

Research Topics

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

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.

Paper Format

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”.

Formatting templates:

Submission webpage

Please submit your papers through the conference submission system here.

Review Process

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)

please contact us Due date for full workshop papers submission
Nov 1, 2023 Notification of paper acceptance to authors
Nov 20,2023 Camera-ready of accepted papers
Dec 15-18 2022 Workshop (one day of)

Workshop Program Co-Chairs

Dr Stephen McGough
Reader in Machine Learning
School of Computing Science
Newcastle University
United Kingdom
E-mail : stephen.mcgough@newcastle.ac.uk

Dr Matthew Forshaw
Reader in Data Science
School of Computing
Newcastle University
United Kingdom
E-mail: matthew.forshaw@newcastle.ac.uk

Dr Amir Atapour Abarghouei
Assistant Professor
Department of Computing Science
Durham University
Durham, DH1 3LE
United Kingdom
Email: Amir.Atapour-Abarghouei@durham.ac.uk

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
Carlos Ordonez University of Houston, USA