Medical Informatics

2010 ◽  
pp. 475-489
Author(s):  
Elif Derya Übeyli
Keyword(s):  
Decision Support ◽  
Information And Communication Technologies ◽  
Medical Informatics ◽  
Preventive Medicine ◽  
Clinical Care ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Related Information ◽  
The People ◽  
Health Related

Information services, medical decision support systems and telemedicine are becoming important tools for medical professionals and also people who are interested in health related information. Medical decision support aims at providing healthcare professionals with therapy guidelines directly at the point of care. Telemedicine is the use of modern information and communication technologies (ICT) for the provision of clinical care to individuals at a distance and transmission of information to provide that care. The chapter intends to an integrated view of the medical informatics and preventive medicine applications via telemedicine. The chapter includes technological perspectives in e-health and telemedicine. The author suggests that the content of the chapter will assist to the people in gaining a better understanding of the technological perspectives in e-health and telemedicine and preventive medicine applications.

scholarly journals Medical Decision Support and Medical Informatics Education: Roots, Methods and Applications in Czechoslovakia and the Czech Republic

2013 ◽  
Vol 22 (01) ◽  
pp. 206-212
Author(s):  
Jana Zvárová
Keyword(s):  
European Union ◽  
Czech Republic ◽  
Decision Support ◽  
Medical Informatics ◽  
Medical Decision ◽  
The Czech Republic ◽  
Medical Decision Support ◽  
History Of ◽  
Academy Of Sciences ◽  
Support Methods

SummaryThe paper describes the history of medical informatics in Czechoslovakia and the Czech Republic. It focuses on the topics of medical informatics education and decision support methods and systems. Several conferences held in Czechoslovakia and in the Czech Republic organized in cooperation with IMIA or EFMI are described. Support of European Union and Czech agencies in several European and national projects focused on medical informatics topics highly contributed to medical informatics development in Czechoslovakia and the Czech Republic and to the establishment of the European Center for Medical Informatics, Statistics and Epidemiology as the joint workplace of Charles University in Prague and Academy of Sciences of the Czech Republic in 1994.

scholarly journals Region Quad-Tree Decomposition Based Edge Detection for Medical Images

2010 ◽  
Vol 4 (1) ◽  
pp. 50-57 ◽  
Author(s):  
Sumeet Dua ◽  
Naveen Kandiraju ◽  
Pradeep Chowriappa
Keyword(s):  
Decision Support ◽  
Edge Detection ◽  
Medical Informatics ◽  
Medical Images ◽  
Medical Decision ◽  
False Alarms ◽  
Image Content ◽  
Medical Decision Support ◽  
Quad Tree ◽  
Efficient Representation

Edge detection in medical images has generated significant interest in the medical informatics community, especially in recent years. With the advent of imaging technology in biomedical and clinical domains, the growth in medical digital images has exceeded our capacity to analyze and store them for efficient representation and retrieval, especially for data mining applications. Medical decision support applications frequently demand the ability to identify and locate sharp discontinuities in an image for feature extraction and interpretation of image content, which can then be exploited for decision support analysis. However, due to the inherent high dimensional nature of the image content and the presence of ill-defined edges, edge detection using classical procedures is difficult, if not impossible, for sensitive and specific medical informatics-based discovery. In this paper, we propose a new edge detection technique based on the regional recursive hierarchical decomposition using quadtree and post-filtration of edges using a finite difference operator. We show that in medical images of common origin, focal and/or penumbral blurred edges can be characterized by an estimable intensity gradient. This gradient can further be used for dismissing false alarms. A detailed validation and comparison with related works on diabetic retinopathy images and CT scan images show that the proposed approach is efficient and accurate.

The Good, the Bad and the Ugly in App Diagnosis: Outcomes and Implications by Example

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 38-38
Author(s):  
Sorana D. Bolboacă ◽  
◽  
Adriana Elena Bulboacă ◽  
◽  
◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision ◽  
Medical Decision ◽  
Normal Body Mass Index ◽  
Disk Herniation ◽  
Scientific Evaluation ◽  
Medical Decision Support ◽  
Lower Back ◽  
Clinical Scenarios ◽  
Medical Decision Support Systems

"The Clinical Decision Support (CDS), a form of artificial intelligence (AI), consider physician expertise and cognitive function along with patient’s data as the input and case-specific medical decision as an output. The improvements in physician’s performances when using a CDS ranges from 13% to 68%. The AI applications are of large interest nowadays, and a lot of effort is also put in the development of IT applications in healthcare. Medical decision support systems for non-medical staff users (MDSS-NMSF) as phone applications are nowadays available on the market. A MDSS-NMSF app is generally not accompanied by a scientific evaluation of the performances, even if they are freely available or not. Two clinical scenarios were created, and Doctor31 retrieved the diagnosis decisions. First scenario: man, 29 years old, and three symptoms: dysphagia, weight loss (normal body mass index), and tiredness. Second scenario: women, 47 years old with L5-S1 disk herniation, abnormal anti-TPO antibodies, lower back pain (burning sensations), constipation, and tiredness. The outcome possible effects and implications, as well as vulnerabilities induced on the used, are highlighted and discussed. "

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