Machine Learning and Big Data in Health Care
Workshop/Special Session on
Machine Learning and Big Data in
Health Care (ML@HC)
In the present era, Machine Learning (ML) has been extensively used for many applications to real world problems. ML techniques are very suitable for Big Data Mining, to extract new knowledge and build predictive models that given a new input can provide in the output a reliable estimate. On the other hand, healthcare is one of the fastest growing data segments of the digital world, with healthcare data increasing at a rate of about 50 % per year. There are three primary sources of big data in healthcare: providers and payers (including EMR, imaging, insurance claims and pharmacy data), -omic data (including genomic, epigenomic, proteomic, and metabolomic data), and patients and non-providers (including data from smart phone and Internet activities sensors and monitoring tools).
The growth of big data in oncology, as well as other severe diseases (such as Alzheimer’s Disease, e.t.c) can provide unprecedented opportunities to explore the biopsychosocial characteristics of these diseases and for descriptive observation, hypothesis generation, and prediction for clinical, research and business issues. The results of big data analyses can be incorporated into standards and guidelines and will directly impact clinical decision making. Oncologists and professionals from related medical fields can increasingly evaluate the results from research studies and commercial analytical products that are based on big data, based on ML techniques. Furthermore, all these applications can be Web-based, so are very useful for the post treatment of the patients.
The aim of this workshop/special session is to serve as an interdisciplinary forum for bringing together specialists from the scientific areas of Computer & Web Engineering, Data Science, Semantic Computing, Bioinformatics-Personalized Medicine, clinicians and caregivers. The focus of this special session is on current technological advances and challenges about the development of big data-driven algorithms, methods and tools; furthermore, to investigate how ML-aware applications can contribute towards Big Data analysis on post treatment follow up.
Topics of interest include, but are not limited to, the following new techniques and applications relevant to big data and semantic analytics:
- Big Data Analytics in Health Care:
- Business intelligence and analytics;
- Visual analytics;
- Data mining;
- Tools and Applications in Health Care:
- Smart Health and Wellbeing;
- AI for post-treatment care;
- Network-based Personalized Medicine;
- Deep Learning
- Professor Spiros Likothanassis, University of Patras, Greece
- Dr K. Votis, Researcher B’, CERTH/ITI, Greece
- Prof. S. Sioutas, University of Patras, Greece
- Prof. K. Tsichlas, University of Patras, Grecce
- Prof. E. Georgopoulos, University of Peloponnese, Greece
- Dr D. Koutsomitropoulos, University of Patras, Greece
- Dr I. Kalamaras, CERTH/ITI, Greece
- Dr A. Scherrer, Fraunhofer Institute for Industrial Mathematics, Germany
- Dr S. Koussouris, Suite5, Cyprus
- T. Zimmerman, Fraunhofer Institute for Industrial Mathematics, Germany
Please visit AIAI 2022 important dates to be informed about the submission deadlines.
Authors are invited to submit original, English-language research contributions or experience reports. Papers should be no longer than 12 pages formatted according to the well-known LNCS Springer style.
Submission details can be found at AIAI conference submission page.
All papers should be submitted either in a doc/docx or in a pdf form and will be peer reviewed by at least 2 academic referees. Contributing authors must follow the AIAI2022’s paper format guidelines as far as the IFIP AICT file format.
You can submit your ML@HC paper here http://www.easyacademia.org/
Submitted papers will be refereed by at least three reviewers for quality, correctness, originality, and relevance. Notification and reviews will be communicated via email. Accepted papers will be presented at the conference and included in the proceedings, which will be published by SPRINGER in the IFIP AICT Lecture Notes.