Biomedical subjects

E Krusinska

Publications and source records attributed to E Krusinska.

3 recordsLinked to original sources

PubMed1993

Influence of "outliers" on the association between laboratory data and histopathological findings in liver biopsy.

Discriminant analysis techniques were used to predict the histopathological findings in liver biopsy specimens in asymptomatic patients with slightly to moderately raised routine liver tests. Moderate to severe fibrosis and/or inflammation were treated as indication for biopsy. Two methods were used to classify patients. One was the dichotomous discrimination between "biopsy necessary" or "biopsy not necessary" groups of patients. The other involved combining two discriminant functions trained separately for recognition of fibrosis or inflammation, and then combined to predict the biopsy necessity. Detection of outliers by standard techniques, directly available in the SPSS-X package, was performed before starting discrimination procedures. Both "sharp" assignment rules and continuous scoring rules were applied to the classification problem. The correct classification rate reached over 85% for the algorithms tested. In the majority of cases the classification was found to be "non-doubtful". Elimination of outliers (especially by standardized residuals) improved the global correct classification rate, but only slightly improved assignment to the "biopsy necessary" group. Routine and complementary laboratory findings were found to be the most discriminating; answers to questionnaire and ultrasound examination were less important. Selection of the most diagnostic features based on "clean" data without outliers enabled us to find interesting medical associations, which were previously masked by extremely asymptomatic values outlying from the main body of the "biopsy necessary" group.

Biopsy↗

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↗