GT3 Decision Support and Recommendation Systems

Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Daniel Sonntag's picture
Daniel Sonntag
German Research Center for AI (DE)
Conference room
Session time
Friday, June 23, 2017 - 09:00 to 10:30
Prognosis of abdominal aortic aneurysms: A machine learning-enabled approach merging clinical, morphometric, biomechanical and texture information
An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac.
Fernando Garcia-Garcia's picture
Fernando Garcia-Garcia
ARTORG Center & University of Bern (CH)
Eleni Metaxa's picture
Eleni Metaxa
Stergios Christodoulidis's picture
Stergios Christodoulidis
Marios Anthimopoulos's picture
Marios Anthimopoulos
Nikolaos Kontopodis's picture
Nikolaos Kontopodis
Martina Correa-Londono's picture
Martina Correa-Londono
Thomas R. Wyss's picture
Thomas R. Wyss
Yannis Papaharilaou's picture
Yannis Papaharilaou
Christos V. Ioannou's picture
Christos V. Ioannou
Hendrik von Tengg-Kobligk's picture
Hendrik von Tengg-Kobligk
Stavroula Mougiakakou's picture
Stavroula Mougiakakou
A non-invasive medical decision support prototype system for Dermatology based on electrical impedance spectroscopy (DermaSense)
Premature detection of malignant melanoma remains the primary factor in reducing mortality from this form of cancer. During the last decade diagnostic sensitivity and specificity have improved through the utilization of new computer-based technologies, which help improve lesion selection for pathology review and biopsy. Despite these advances in melanoma diagnosis, initial detection, timely recognition and quick treatment of melanoma remain crucial.
Alexander Zogkas's picture
Alexander Zogkas
Sotiria Gilou's picture
Sotiria Gilou
Aristotle University of Thessaloniki (GR)
Inessa Kirsanidou's picture
Inessa Kirsanidou
Chrysovalantis Korfitis's picture
Chrysovalantis Korfitis
Christina Kemanetzi's picture
Christina Kemanetzi
Elizabeth Lazaridou's picture
Elizabeth Lazaridou
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Is a Decision Support System Based on Robson's Classification Enough to Reduce Cesarean Section Rates?
The cesarean section (CS) rates are important global indicators for measuring the access to obstetric services. In 2001, Robson proposed a CS classification in ten-groups as the most appropriate to compare surgery rates. However, having a decisional support system from Robson's Classification is enough to reduce CS rates? The births analysis that occurred in 2016, inside a public hospital maternity, showed 1,946 deliveries of which 35.7% were CS with a positive growth trend (R2 = 0.137).
Juliano Gaspar's picture
Juliano Gaspar
Universidade Federal de Minas Gerais (BR)
Zilma Reis's picture
Zilma Reis
Juliana Barra's picture
Juliana Barra
Design and Development of a Mobile Decision Support System: Guiding Clinicians Regarding Law in the Practice of Psychiatry in Emergency Department
Decision-making in an emergency department needs to be efficient. It does not allow observation of the patient for a prolonged period of time, especially if the patients harm themselves or others, or refuses treatment. This includes suicidal, violent, intentional self-inflicted or non-consenting to treatments’ patient. Clinicians have to quickly decide whether to call the police, admit the patient to the psychiatric ward, according to recommended, predefined procedures.
Soudabeh Khodambashi's picture
Soudabeh Khodambashi
Norwegian University of Science and Technology (NO)
Florentin Moser's picture
Florentin Moser
Jon Atle Gulla's picture
Jon Atle Gulla
Pekka Abrahamsson's picture
Pekka Abrahamsson
Integrated decision support by combining textual information extraction, facetted search and information visualisation
This work focusses on our integration steps of complex and partly unstructured medical data into a clinical research database with subsequent decision support. Our main application is an integrated facetted search tool, followed by information visualisation based on automatic information extraction results from textual documents. We describe the details of our technical architecture (open-source tools), to be replicated at other universities, research institutes, or hospitals.
Daniel Sonntag's picture
Daniel Sonntag
German Research Center for AI (DE)
Hans-Jurgen Profitlich's picture
Hans-Jurgen Profitlich
Predicting Sepsis Biomarker Progression under Therapy
Sepsis is a serious, life-threatening condition that presents a growing problem in medicine and health-care. It is characterized by quick progression and high variability in the disease manifestation, so treatment should be personalized and tailored to fit individual characteristics of a particular subject. That requires close monitoring of the patient's state and reliable predictions of how the targeted therapy will affect sepsis progression over time.
Ivan Stojkovic's picture
Ivan Stojkovic
Temple University (USA)
Zoran Obradovic's picture
Zoran Obradovic
Temple University (USA)