CALL FOR PAPERS
Virtual Special Session 'Machine Learning in Health'@ ICMLA 2023
Virtual Special Session at the 22nd IEEE International Conference on Machine Learning and Applications
Format: Online Special Session
SPECIAL SESSION DATE: December 15-17, 2023
LOCATION: Hyatt Regency Jacksonvill Riverfront, Florida
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.
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 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.
Format: Online Special Session
Ester ZUMPANO, DIMES University of Calabria, Italy, [log in to unmask]
Carmela COMITO, CNR-ICAR, Italy, [log in to unmask]
Agostino FORESTIERO, CNR-ICAR, Italy, [log in to unmask]
Papers submitted for reviewing should conform to IEEE specifications with
maximum length of 6 pages including references and any other additional
material (up to 2 pages could be added with extra charge, please refer to
registration page for details). Manuscript templates can be downloaded from
IEEE website (http://www.ieee.org/conferences_events/conferences/publishing/templates.html).
All submissions must be anonymized and may not contain any information with the intention
or consequence of violating the double-blind reviewing policy, including (but not limited to)
citing previous works of the authors or sharing links in a way that can infer any author’s
identity or institution,
actions that reveal the identities of the authors to potential reviewers.
Papers must be submitted via the CMT System. Please follow the
link (https://cmt3.research.microsoft.com/ICMLA2023/) and select
the track “Special Session on Machine Learning in Health”.
All accepted papers must be presented by one of the authors,
who must register.
- Submission deadline: September 5, 2023
- Acceptance/Rejection Notification: September 25, 2023
- Camera-ready & Pre-Registration: October 5, 2023
Accepted papers will be published in the IEEE ICMLA 2023
conference proceedings (published by IEEE). A selected number
of accepted papers will be invited for possible inclusion, in
an expanded and revised form,
in some journal special issues.
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