Biomedical subjects

G Surján

Publications and source records attributed to G Surján.

6 recordsLinked to original sources

PubMed1997

TSMI: a CEN/TC251 standard for time specific problems in healthcare informatics and telematics.

Time is the most important variable in healthcare, and standards are needed about how to represent information with explicit references to time. In this paper, the European Prestandard 'TSMI: time standards for healthcare specific problems' (CEN/TC251 preENV 12381) is presented which aims to be the first contribution to this harmonisation process, focusing on 'representation' and 'explicit reference' of temporal information in healthcare. The prestandard is mainly composed of two parts. First, the basic building blocks for modelling time-related information are introduced, and a formal representation scheme proposed. In a second part, conformance rules and principles for Healthcare Data and Information as well as for Healthcare Information Systems, are covered.

Artificial Intelligence↗

PubMed1995

[Method for the determination of the functional degree of esophageal stricture and the effectiveness of dilatation].

Based on their experiences of 494 oesophagus dilatation in 98 patients, authors developed a stadium system for defining the severity of dysphagia caused by oesophagus stenosis of stricture. This system is usable for follow-up the course of the disease, for the establishing the necessity of the dilatation, and for the comparison of results achieved by different dilatation methods, as well. The method is based on simple, well-defined clinical parameters, special skills or instrumentations not required, and fits the clinical experiences of many years of the authors. For this reasons it is useful both general practitioners and for the specialists performing the esophagus dilatation, as well.

Adult↗

PubMed1995

Third generation electronic medical record knowledge based perspectives.

There is a need to develop better electronic medical records. One possible solution is to put more and more 'routine' medical knowledge into systems handling medical records. In this paper, we analyze the current state-of-the-art of knowledge-based medical record handling; we mainly consider the work of Rector et al. [1]. We offer a more detailed 'four level' knowledge level model compared to the 'two level' model of Rector. The EMR of the future might be approached with a top-down method, using the above mentioned 'four level' model.

Artificial Intelligence↗

PubMedDate not supplied

Towards a quantitative approach of medical information. Part 1. Measures of a multidimensional medical information space.

Despite the importance of quantitative analysis of medical information, it is a rare subject in informatics literature. To stimulate more interest the authors propose a multidimensional medical information space, in which measures can be defined to compare different medical information objects or knowledge areas. To describe the measures, classical information theory, the general database theory of Sundgren and the Blois model of medical thinking are used. A space model of (at least) three dimensions is offered, where information objects might have a normalized length, a total depth (measuring embedded knowledge levels) and a complexity width.

Computer-Assisted Instruction↗

PubMedDate not supplied

Towards a quantitative approach of medical information. Part 2. Comparative measurement of medical information objects.

One of the key problems in medical knowledge representation is that we usually have no idea about the largeness of the knowledge to be represented. Underestimation of this largeness may occur frequently. Considering this situation, the authors have applied a method of measuring or estimating the largeness of a certain medical knowledge area modelled in a theoretical information space. The method is tested on two sets of terms, the OMED terminology of digestive endoscopy and on the second and third version of the SNOMED nomenclature. The assessment of largeness of a knowledge area does not seem possible by a single measure. The 'volume', 'density' and 'complexity' must be addressed separately. In the present study different medical knowledge-representation systems are compared according to their volume. Possible ways to estimate their complexity and density are mentioned.

Decision Making, Computer-Assisted↗

PubMedDate not supplied

Theoretical considerations on medical concept representation.

Concepts are seen as building elements of more compositional information objects in medicine. Consequently, representation of medical concepts plays a critical role in any information system. The authors present some of the key problems which need solution, such as definition of medical domain, analysis of internal structure, different subsets of medical concepts, and the problem of the 'elementary' concepts. As a result of these considerations we can conclude that the domain of medical concepts can hardly be delimited and consists of different subsets. These subsets need different representation methods according to their different nature. Some of these subsets have a hierarchic structure. In those one can find sometimes multiple hierarchies. We suggest avoiding multiple hierarchies in concept systems by the introduction of new dimensions.

Decision Support Techniques↗