GDV images: Current research and results

We use statistical analysis and machine learning to interpret the GDV coronas of fruits and human’s fingers in order to verify two hypotheses: (A) the GDV images contain useful information about the object/patient and (B) the human bioelectromagnetic field can be influenced by some outside factors. We performed several independent studies, three of which we here briefly describe: (a) recording coronas of berries of different grapevines, (b) detecting the influence of drinking the tap water from ordinary glass and energetic glass K2000, and (c) detecting the influence of natural energy source in Tunjice near Kamnik, Slovenia on the human bioelectromagnetic field. All three studies, as well as some other studies described elsewhere, gave significant results and therefore support both hypotheses

Evidence-based clinical knowledge assistance towards supplementing patient referral letters for evidence informed decision making

Referral letters are common means by which healthcare practitioners exchange information relevant to patient care. It has been argued that information contained in letters of referral and reply often does not meet the information needs of letter receipting.GPs (general practitioners) and specialist require knowledge and information at point of care that is related to patient specification to narrow the information gap and to assist towards better interpretation of referral letters.Given this problem at hand we present, in this paper ECKA (Evidence based clinical knowledge assistance) framework that provides evidence based clinical knowledge assistance from clinical practice guidelines and medical explicit knowledge in terms of medical literature from Medline/Pubmed pertaining to referral letter.This will help narrow the information gap at point of care and to provide better interpretation of referral letters

Machine learning and GDV images: current research and results

We use machine learning to interpret the GDV coronas of human's fingers in order to verify three hypotheses: 1. The GDV images contain useful information about the object/patient, 2. The map of coronas of fingers according to Chinese medicine does make sense, and 3. The human bioelectric field can be influenced by some outside factors, such as special T-shirts. We performed three independent studies: (a) recording coronas of apple skin, in order to verify if we can obtain any useful information for distinguishing the sort, age and the sun/shadow part of the apple, (b) detecting the state of menstrual cycle for females, and (c) detecting of the influence of different T-shirts on human's bioelectric field

Learning to classify x-ray images using relational learning

Abstract: Image understanding often requires extensive background knowledge. The problem addressed in this paper is such knowledge can be acquired. We discuss how relational machine learning methods can be used to automatically build rules for classifying types of blood vessels. We introduce a new learning system that can make use of background knowledge coded as arbitrarily complex Prolog programs to construct concept descriptions, particularly those needed to classify features in an image.

Learning to Recognize Objects - Toward Automatic Calibration of Colour Vision for Sony Robots

Color detection can be seriously affected by lighting conditions and other variations in the environment. The robot vision systems need to be recalibrated as lighting conditions change, otherwise they fail to recognize objects or classify them incorrectly. This paper describes experiments toward object recognition under different lightning conditions. We propose to train the vision system to recognize objects under different lightning conditions using machine learning. Learned knowledge is then used for object recognition. Having attached leaning module to the vision system facilitates the object recognition and provides conditions for automatic adaptation of the vision system to new environment. 1

Bronchopulmonary Segments Approximation using Anatomical Atlas

Bronchopulmonary segments are valuable as they give more accurate localization than lung lobes. Traditionally, determining the segments requires segmentation and identification of segmental bronchi, which, in turn, require volumetric imaging data. In this paper, we present a method for approximating the bronchopulmonary segments for sparse data by effectively using an anatomical atlas. The atlas is constructed from a volumetric data and contains accurate information about bronchopulmonary segments. A new ray-tracing based image registration is developed for transferring the information from the atlas to a query image. Results show that the method is able to approximate the segments using sparse HRCT data with slice gap up to 25 millimeters

E-healthcare for diabetes mellitus type 2 patients – a randomised controlled trial in Slovenia

Telemonitoring and web-based interventions are increasingly used in primary-care practices in many countries for more effective management of patients with diabetes mellitus (DM). A new approach in treating patients with diabetes mellitus in family practices, based on ICT use and nurse practitioners, has been introduced and evaluated in this study