Biomedical subjects

U Mathiesen

Publications and source records attributed to U Mathiesen.

5 recordsLinked to original sources

PubMed1998

Assessing an AI knowledge-base for asymptomatic liver diseases.

Discovering not yet seen knowledge from clinical data is of importance in the field of asymptomatic liver diseases. Avoidance of liver biopsy which is used as the ultimate confirmation of diagnosis by making the decision based on relevant laboratory findings only, would be considered an essential support. The system based on Quinlan's ID3 algorithm was simple and efficient in extracting the sought knowledge. Basic principles of applying the AI systems are therefore described and complemented with medical evaluation. Some of the diagnostic rules were found to be useful as decision algorithms i.e. they could be directly applied in clinical work and made a part of the knowledge-base of the Liver Guide, an automated decision support system.

Algorithms↗

PubMed1996

Hepatitis C virus transmission, 1988-1991, via blood components from donors subsequently found to be anti-HCV-positive.

The recipients of blood components, from the first 12 anti-hepatitis C virus (HCV) positive donors identified by blood donor screening, 1985-1991, were traced retrospectively and tested to assess the HCV transmission rate, HCV genotypes and disease severity. Three enzyme-linked immunosorbent assay (ELISA) positive but RIBA-indeterminate and HCV RNA-negative donors did not transmit HCV to their 9 traced recipients. Nine RIBA- and HCV RNA-positive donors had donated blood to 27 now living recipients of whom 16/27 (59%) were viraemic 1-5 years later. Nine recipients had resolved infection, as determined by PCR HCV RNA. Five of these were RIBA-2 positive but HCV RNA-negative and 4 recipients were RIBA-2-indeterminate and HCV RNA-negative. Two recipients negative in all tests had probably received blood before the donor became infected with HCV. The HCV genotype in each case was identical between the donor and the recipient. Of the viraemic recipients, 50% (8/16) were unsuitable for further investigation or therapy due to their high age and/or underlying severe disease. At most, only 30% (8/27) of the recipients were suitable for further investigation and/or treatment. Two of these were already diagnosed as being infected with HCV before being traced. It is concluded that the benefit of a general tracing of recipients of blood components from HCV-infected donors is doubtful since only a few of them are suitable candidates for treatment. Our results seem to indicate that it is more appropriate to recommend anti-HCV testing to those seeking medical care who have received transfusions or undergone major surgery before 1992, i.e. before anti-HCV-screening was initiated.

Adolescent↗

PubMed1995

Machine learning to support diagnostics in the domain of asymptomatic liver disease.

Machine learning procedures, in unsupervised and supervised manner, can enable their users to achieve knowledge hardly comprehensible by even the best experts. This is true also if the clinical knowledge has been carefully assembled in a prospective way. A data set including 165 patients with elevated routine laboratory tests was extensively studied according to clinical history, laboratory profile and liver biopsy. Unsupervised learning by Kohonen feature map disclosed 4 groups of patients: the largest one with no or slight histopathological changes (116) and three smaller, more homogenous, with more diseased patients. Standardized histopathological scorings of the liver specimens defined patients into two groups. Fifty-eight of them were, according to the analysis, recommended for a liver biopsy, due to more severe degrees of inflammation and fibrosis. One-hundred and seven of the patients, in whom liver biopsy was retrospectively considered unnecessary, had only minor degrees of inflammation, fibrosis and/or steatosis. Supervised learning, using the inductive systems based on Quinlan's ID3 and CART algorithms, extracted knowledge in the form of decision trees. This approach could define a need for biopsy either with a very few significant findings or by pathways containing quotients and multiplications of the different basic items. These procedures were analyzed and compared for their theoretical and applicative performances. The cluster and Fischerian discriminant analyses were performed in order to compare the classification performance. The medical appropriateness of the obtained results is satisfying, therefore decision support systems, outlined in this study, should be evaluated in wider clinical practice. To achieve this goal, an example of a Medical Logical Module (MLM), based on the Arden Syntax, is given.

Adult↗

PubMed1991

Integrated approach for designing medical decision support systems with knowledge extracted from clinical databases by statistical methods.

In clinical research data is often studied by a particular method without previous analysis of quality or semantic contents which could link clinical database and data analytical (e.g. statistical) procedures. In order to avoid bias caused by this situation, we propose that the analysis of medical data should be divided into two main steps. In the first one we concentrate on conducting the quality, semantic and structure analyses. In the second step our aim is to build an appropriate dictionary of data analysis methods for further knowledge extraction. Methods like robust statistical techniques, procedures for mixed continuous and discrete data, fuzzy linguistic approach, machine learning and neural networks can be included. The results may be evaluated both using test samples and applying other relevant data-analytical techniques to the particular problem under the study.

Artificial Intelligence↗

PubMedDate not supplied

A statistically rule-based decision support system for the management of patients with suspected liver disease.

The paper describes how a decision support system in liver diseases, mostly oriented to prediction of the necessity for liver biopsy, has been developed. The system designed is a hybrid one and consists of two parts: logical and statistical. The logical part contains rules, formulated on the basis of current medical knowledge, which enables recognition of clear cases; diseased or non-diseased. The unclear cases are classified on the basis of rules statistically extracted from databases. These rules have been reached after a comprehensive exploratory analysis of the sample of 165 patients with slightly to moderately raised levels of routine liver tests but without signs or symptoms of liver diseases. The extracted decision diagrams which simulate traditional medical diagnosis conduct have been found to be superior to discriminant analysis and probabilistic inductive learning. They use only a limited number of laboratory tests to detect the necessity for biopsy.

Algorithms↗