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Virtual Special Session 12:
Machine Learning in Health

Machine learning and artificial intelligence are changing the landscape of healthcare and modern personalized precision medicine. The increasing availability of health data, including patient medical records also obtained by wearable sensors, medical imaging, health insurance claims, surveillance, together with the rapid progress of machine learning algorithms and analysis techniques, are gradually enabling doctors for better diagnosis, improve disease surveillance, facilitating early disease detection, uncovering novel treatments and drug-interaction, detect false alarms and over-diagnosis, and creating an era of truly personalized medicine. A great challenge is build better modeling tools for integrating human expertise and machine learning techniques to exploit big data in healthcare, and formulate hypothesis about how the human organisms act in heath and illness.

Scope:

The main areas of machine learning and AI applications in healthcare are: personalized precision medicine, analysis and interpretation of radiology images, automated diagnosis, prescription preparation, clinical workflow monitoring, patient monitoring and care, discovery of new drugs, predicting the impact of gene edits, treatment protocol development, early diagnoses of diseases. In this context, modern machine learning techniques can play a crucial role to deal with such amount of heterogeneous, multi-scale and multi-modal data. Some examples of techniques that are gaining attention in this domain include deep learning, domain adaptation, semi-supervised approach, time series analysis and active learning. Even though the use of machine learning and the development of ad-hoc techniques are gaining increasing popularity in the health domain, we can witness that a significant lack of interaction between domain experts and machine learning researchers still exists. The special session provides a venue for the community to promote collaborations and present and exchanges ideas, practices and advances specific to machine learning use in the particularly challenging area of health. The goal is to bring people in the field cross-cutting information management and medical informatics to discuss innovative data management and analytics technologies highlighting end-to-end applications, systems, and methods to address problems in healthcare, public health, and everyday wellness, with clinical, physiological, imaging, behavioral, environmental, and omic data, and data from social media and the Web.

The special session solicits empirical, experimental, methodological, and theoretical research reporting original and unpublished results on topics in the realm of healthcare and health informatics along with applications to real life situations. This can mean new models, new datasets, new algorithms, or new applications.

Topics:

Topics of interest include, but are not limited to:

• Personal health virtual assistant
• Early disease diagnosis and treatment prediction
• Clinical decision support in disease diagnosis and treatment
• Analysis and interpretation of radiology images
• Application of deep learning methods to health data
• Spatio-temporal prediction of pandemics
• Modeling the health status and well-being of individuals
• Real-time syndromic surveillance and early detection of emerging disease
• Drug adversial reaction
• Drug abuse and alcoholism incidence monitoring
• Medical imaging analysis and diagnosis assistance
• mHealth, eHealth, and Wearable Health
• Blockchain for healthcare
• Social media data analys and mining for public health
• Novel methods and frameworks for mining and integrating big health data
• Semantics and interoperability for healthcare data
• Clinical natural language processing and text mining
• Predictive modelling for diagnosis and treatment
• Data privacy and security for healthcare data
• Medical fraud detection
• Data analytics for pervasive computing for medical care.

Chairs: Carmela Comito, Agostino Forestiero, Ester Zumpano

Bio: Carmela Comito is a researcher at the Institute of High Performance Computing and Networking of the Italian National Research Council (ICAR-CNR), Italy. She received her Master’s degree in Computer Engineering and her Ph.D. in Systems and Computer Engineering from the University of Calabria, Italy. In 2006 she was a visiting researcher at the School of Computer Science of the University of Manchester, UK, and in 2017 she was a visiting researcher at LIRMM, University of Montpellier, France. She is adjunct professor at University of Calabria. She co- authored over 80 peer-reviews papers in international journals, conference proceedings, and edited volumes. Her research interests include artificial intelligence, urban computing, big data analysis and mining, social network data analysis and mining, health informatics. Agostino Forestiero is a researcher at the Institute for High Performance Computing and Networking of National Research Council of Italy, (ICAR-CNR), Cosenza, since 2010. He received his Ph.D. in Computer Engineering (2006) and the Master Degree in Computer Engineering (2002) from the University of Calabria. He received the National Scientific Habilitation for the role of associate professor of Information Processing Systems (09/H1), Italian Ministry of University and Research (2018). He published more than 100 scientific papers on international conferences and journals Ester Zumpano is an associate professor of Computer Engineering at the Department of Computer Engineering, Modeling, Electronics and Systems Engineering, University of Calabria (DIMES). She received a Phd in Computer Science from the University of Calabria in 2003. Her areas of research include health information systems, data integration, logic programming, view updating, distributed systems, artificial Intelligence, database management. She is member of the Scientific Board of the Ph.D. Course in Information andCommunication Technologies, University of Calabria, Italy.

Technical Committee (tentative)

• Mehdi Sheikhalishahi, InnoTec21 GmbH, Germany Marinella Petrocchi, ISTI-CNR, Italy
• Andrea Calì, University of London, UK
• Cinzia Cappiello, Politecnico of Milano, Italy
• Clara Pizzuti, ICAR-CNR, Italy
• David Manset, GNUBILA/MAAT France
• Massimo Esposito, ICAR-CNR, Italy
• Silvia Miri, University of Bologna, Italy
• Leopoldo Bertossi, Carleton University, Ottawa, Canada Shahaboddin Shamshirband, Ton DucThang University, Viet Nam Domenico Ursino, Università Politecnica delle Marche, Italy
• Luca Virgili, Università Politecnica delle Marche, Italy
• Eugenio Vocaturo, University of Calabria, Italy
• Luciano Caroprese, University of Chieti-Pescara, Italy
• Elio Masciari, Università degli Studi di Napoli Federico II, Italy

Paper Submission Instructions

All papers will be double-blind reviewed and must present original work.

• CMT Submission Site
• Select the track: Virtual Special Session 12: Machine Learning in Health

Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from:

• IEEE website

Keydates

• Submission due date: September 5, 2023
• Notification of Acceptance: September 25, 2023
• Camera Ready Papers: October 5, 2023
• Pre-registration: October 15, 2023
• Conference: December 15-17, 2023

Registration

In order for your paper to be presented in the virtual session and published in the proceedings you must register to the conference.

Paper Presentation Instructions

The papers submitted to this track will be presented in the virtual part of the conference. There is no in-person presentation for this session.

If you decide to participate in-person to the conference you must have an adequate internet connection for your presentation and to participate in this special session.