Studies in health technology and informatics
Jan 1, 2015
There are several types of Diagnostic Decision Support Systems (DDSS) but all move towards a common direction: provide assistance to the doctors/clinicians to make the right diagnosis for a specific patient, minimizing as much as possible the needed ...
Studies in health technology and informatics
Jan 1, 2015
A machine-learning framework to identify the specific disease afflicting certain patients diagnosed with Neurological and Neuromuscular Diseases (NND) or Juvenile Idiopathic Arthritis (JIA) using only gait analysis data is presented. Classifying such...
Studies in health technology and informatics
Jan 1, 2015
Reasoning conducted in clinical practice is manifested through different and often combined reasoning and learning strategies, adjusted to the characteristics of the available information, the medical professional's experience and skills, and the ava...
Studies in health technology and informatics
Jan 1, 2015
This paper presents novel GA-ANFIS expert system prototype for dermatological disease detection by using dermatological features and diagnoses collected in real conditions. Nine dermatological features are used as inputs to classifiers that are based...
Studies in health technology and informatics
Jan 1, 2015
Electronic Health Records (EHRs) contain a wealth of information about an individual patient's diagnosis, treatment and health outcomes. This information can be leveraged effectively to identify patients who are similar to each for disease diagnosis ...
Studies in health technology and informatics
Jan 1, 2015
Traditional methods of rehabilitation require continuous attention of therapists during the therapy sessions. This is a hard and expensive task in terms of time and effort. In many cases, the therapeutic objectives cannot be achieved due to the overw...
Studies in health technology and informatics
Jan 1, 2015
The aim of this research is to develop a novel GA-ANFIS expert system prototype for classifying heart disease degree of a patient by using heart diseases attributes (features) and diagnoses taken in the real conditions. Thirteen attributes have been ...
OBJECTIVE: The purpose of the proposed study was to develop an identification unit for classifying periodontal diseases using support vector machine (SVM), decision tree (DT), and artificial neural networks (ANNs).
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