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Special Sessions

Special sessions are very small and specialized events to be held during the conference as a set of oral and poster presentations that are highly specialized in some particular theme or consisting of the works of some particular international project. The goal of special sessions (minimum 4 papers; maximum 9) is to provide a focused discussion on innovative topics. All accepted papers will be published in a special section of the conference proceedings book, under an ISBN reference, and on digital support. All papers presented at the conference venue will be available at the SCITEPRESS Digital Library. SCITEPRESS is a member of CrossRef and every paper is given a DOI (Digital Object Identifier). The proceedings are submitted for indexation by The DBLP Computer Science Bibliography, Conference Proceedings Citation Index, Engineering Index (EI), SCOPUS, Semantic Scholar, Google Scholar and Microsoft Academic.


SPECIAL SESSIONS LIST

ClinMed 2020Special Session on Designing Future Health Innovations as Needed
Chair(s): Lionel Pazart and Robert Picard

NDNSNT 2020Special Session on Non-invasive Diagnosis and Neuro-stimulation in Neurorehabilitation Tasks
Chair(s): Vladimir Kublanov, Yury Kistenev and Zafar Yuldashev

SERPICO 2020Special Session on Mining Self-reported Outcome Measures, Clinical Assessments, and Non-invasive Sensor Data Towards Facilitating Diagnosis, Longitudinal Monitoring, and Treatment
Chair(s): Athanasios Tsanas, Andreas Triantafyllidis, Georgios Theodorakopoulos and Siddharth Arora

Cognitive Health IT 2020Special Session on Machine Learning and Deep Learning Improve Preventive and Personalized Healthcare
Chair(s): Tahir Hameed and Syed Ahmad Chan Bukhari

Special Session on Designing Future Health Innovations as Needed - ClinMed 2020

Paper Submission: December 19, 2019 (expired)
Authors Notification: January 9, 2020 (expired)
Camera Ready and Registration: January 17, 2020 (expired)


Co-chairs

Lionel Pazart
Tech4Health/F-CRIN
France
e-mail
 
Robert Picard
Forum LLSA – Forum of Living Labs in Health & Autonomy
France
e-mail
 
Scope

Patients expectations for care and care delivery are changing and force the industry to change. How to bring value with new ideas? How to lead them to maturity? How to get to the market in a sustainable way with a truly useful product? Innovation by Design refers to needs-driven projects that are created to address a recognised market need or societal problem, to deliver tangible results for citizens. Deciphering real needs by observing existing practices and discussing with end-users (patients, professionals) can contribute to the well design of new medical devices by providing the relevant technical specifications. The conference will take a multi-perspective view about what is needed in order to move technology along to real sustained and widespread use.


Special Session on Non-invasive Diagnosis and Neuro-stimulation in Neurorehabilitation Tasks - NDNSNT 2020

Paper Submission: December 19, 2019 (expired)
Authors Notification: January 9, 2020 (expired)
Camera Ready and Registration: January 17, 2020 (expired)


Co-chairs

Vladimir Kublanov
Ural Federal University
Russian Federation
e-mail
 
Yury Kistenev
National Research Tomsk State University
Russian Federation
e-mail
 
Zafar Yuldashev
Saint-Petersburg Electrotechnical University ETU "LETI"
Russian Federation
e-mail
 
Scope

Among the leading causes of disability and high mortality are diseases of the central nervous system that are accompanied by cognitive, sensor, motor and autonomic disorders. For treatment of such diseases pharmacological methods are not always in conformity with the contemporary requirements of efficacy and safety of health care. New technologies for diagnosis and neuroprotection are emerging, based on application of different physical fields for formation of the neuromodulation as well as methods for its visualization. Especially promising is combined application. Participation in this Special Session of scientist, involved in various research directions of diagnosis and neuro-stimulation theory and techniques, will allow formulating new strategies for diagnosis and treatment process, aimed to minimize health risks for each person, based on personalized strategies of prevention and therapy.




Special Session on Mining Self-reported Outcome Measures, Clinical Assessments, and Non-invasive Sensor Data Towards Facilitating Diagnosis, Longitudinal Monitoring, and Treatment - SERPICO 2020

Paper Submission: December 19, 2019 (expired)
Authors Notification: January 9, 2020 (expired)
Camera Ready and Registration: January 17, 2020 (expired)


Co-chairs

Athanasios Tsanas
University of Edinburgh
United Kingdom
e-mail
 
Andreas Triantafyllidis
Information Technologies Institute, CERTH
Greece
e-mail
 
Georgios Theodorakopoulos
Cardiff University
United Kingdom
e-mail
 
Siddharth Arora
University of Oxford
United Kingdom
e-mail
 
Scope

There is increasingly available data to support more reliable, evidence-based clinical assessment towards diagnosis, monitoring disease trajectory, and rehabilitation. These come from a variety of sources, including self-reports from patients, clinician-generated reports, wearable devices, and smartphones. This special session will cover new developments towards mining Patient Reported Outcome Measures (PROMs), clinical assessments (both longitudinal and also multimodal assessments), and sensor-based datasets. Moreover, we welcome submissions covering data anonymization and privacy of medical data.


Special Session on Machine Learning and Deep Learning Improve Preventive and Personalized Healthcare - Cognitive Health IT 2020

Paper Submission: December 28, 2019 (expired)
Authors Notification: January 9, 2020 (expired)
Camera Ready and Registration: January 17, 2020 (expired)


Co-chairs

Tahir Hameed
Merrimack College
United States
e-mail
 
Syed Ahmad Chan Bukhari
St.John's University
United States
e-mail
 
Scope

Machine learning has transformed healthcare by improving disease prediction, diagnosis, prognosis, and treatments. Using large but relatively structured datasets like electronic health records (EHRs), scans, and labs, they provide indispensable tools and decision support to healthcare providers and patients. Lately, with bigger, more complex and unstructured datasets available, healthcare apps and clinical decision support systems (CDSS) have started to leverage deep learning to refine these recommendations. Such systems not only have prediction but learning capabilities also. Consequently, they enable preventive and rehabilitative healthcare that is highly personalized and adaptive. This session seeks completed research on applications of deep learning and cognitive computing in preventive care, personalized treatments and adaptive CDSS aiming to better health outcomes, patient satisfaction and costs.























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