Peroxisome proliferator-activated receptor gamma/Pro12Ala polymorphism and peroxisome proliferator-activated receptor gamma coactivator-1 alpha/Gly482Ser polymorphism in patients with sarcoidosis.

BACKGROUND AND AIM OF THE WORK: Reduced expression and activity of the peroxisome proliferator-activated receptor gamma (PPARG) have been measured in cells of bronchoalveolar lavage fluid in sarcoidosis patients. PPARG, together with its transcriptional coactivator peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PPARGC1A), has important modulating effects on immune response and apoptosis. In the present study, we investigated whether the polymorphisms Pro12Ala (rs1805192) in the PPARG gene and Gly482Ser (rs8192678) in the PPARGC1A gene, which affect transcriptional activities, are associated with sarcoidosis. METHODS: We performed an integrative "omic" approach and identified the PPARG gene as a suitable candidate. Polymerase chain reaction was performed followed by restriction fragment length polymorphism to determine PPARG/Pro12Ala and PPARGC1A/Gly482Ser genotypes of 104 sarcoidosis patients and 112 healthy control subjects. RESULTS: A higher frequency of the Ala allele (p=0.0101, OR=1.84, CI 1.18-2.88), as well as a significantly higher frequency of Pro/Ala heterozygotes and Ala/Ala homozygotes at the Pro12Ala/PPARG polymorphism (p=0.0020, OR=2.45, CI 1.42-4.25) were found in patients with sarcoidosis. In addition, a higher frequency of the Ser allele (p=0.013, OR=1.69, CI 1.13-2.53) and Gly/Ser heterozygotes and Ser/Ser homozygotes (p=0.0470, OR=1.80, CI 1.04-3.10) at the Gly482Ser/PPARGC1A polymorphism were found in patients with sarcoidosis as compared to healthy control subjects. CONCLUSION: Our results indicate that the presence of the Ala allele at the PPARG/Pro12Ala polymorphism and the Ser allele at the PPARGC1A/Gly482Ser polymorphism may be a predisposing factor for sarcoidosis.

Accuracy of intelligent medical systems.

Intelligent medical systems are a special kind of medical software in general, and just as any medical software system they should make accurate presumptions. However, accuracy of intelligent medical systems is highly dependent on various factors such as: choosing an appropriate basic method (i.e. decision trees, neural networks), induction method (i.e. purity measures) and appropriate support methods (i.e. discretization, pruning, boosting). In this paper we present the results of extensive research of the above alternatives on 54 UCI databases and their influence on the accuracy of decision trees, which constitute one of the most desirable forms of intelligent medical systems. We also introduce new hybrid purity measures that on some databases outperform other purity measures. The results presented here show that the selection of the right purity measure with the proper discretization method and application of the boosting method can really make a difference in terms of higher accuracy of induced decision trees. Thereafter choosing the appropriate factors that can increase the accuracy of the induced decision tree is a very demanding and time-consuming task.

Supporting medical decisions with vector decision trees.

The article presents the extension of a common decision tree concept to a multidimensional - vector - decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample. It has the functionality of many separate decision trees acting on a same set of training data and answering different questions. Vector decision tree is therefore simple in its form, is easy to use and analyse and can express some relationships between decisions not visible before. To explore and test the possibilities of this concept we developed a software tool--DecRain--for building vector decision trees using the ideas of evolutionary computing. Generated vector decision trees showed good results in comparison to classical decision trees. The concept of vector decision trees can be safely and effectively used in any decision making process.

Comparison of three databases with a decision tree approach in the medical field of acute appendicitis.

Decision trees have been successfully used for years in many medical decision making applications. Transparent representation of acquired knowledge and fast algorithms made decision trees one of the most often used symbolic machine learning approaches. This paper concentrates on the problem of separating acute appendicitis, which is a special problem of acute abdominal pain from other diseases that cause acute abdominal pain by use of an decision tree approach. Early and accurate diagnosing of acute appendicitis is still a difficult and challenging problem in everyday clinical routine. An important factor in the error rate is poor discrimination between acute appendicitis and other diseases that cause acute abdominal pain. This error rate is still high, despite considerable improvements in history-taking and clinical examination, computer-aided decision-support and special investigation, such as ultrasound. We investigated three different large databases with cases of acute abdominal pain to complete this task as successful as possible. The results show that the size of the database does not necessary directly influence the success of the decision tree built on it. Surprisingly we got the best results from the decision trees built on the smallest and the biggest database, where the database with medium size (relative to the other two) was not so successful. Despite that we were able to produce decision tree classifiers that were capable of producing correct decisions on test data sets with accuracy up to 84%, sensitivity to acute appendicitis up to 90%, and specificity up to 80% on the same test set.

Finding the right decision tree's induction strategy for a hard real world problem.

Decision trees have been already successfully used in medicine, but as in traditional statistics, some hard real world problems can not be solved successfully using the traditional way of induction. In our experiments we tested various methods for building univariate decision trees in order to find the best induction strategy. On a hard real world problem of the Orthopaedic fracture data with 2637 cases, described by 23 attributes and a decision with three possible values, we built decision trees with four classical approaches, one hybrid approach where we combined neural networks and decision trees, and with an evolutionary approach. The results show that all approaches had problems with either accuracy, sensitivity, or decision tree size. The comparison shows that the best compromise in hard real world problem decision trees building is the evolutionary approach.

The art of building decision trees.

Decision support systems that help physicians are becoming a very important part of medical decision making. They are based on different models and the best of them are providing an explanation together with an accurate, reliable, and quick response. One of the most viable among models are decision trees, already successfully used for many medical decision-making purposes. Although effective and reliable, the traditional decision tree construction approach still contains several deficiencies. Therefore we decided to develop and compare several decision support models using four different approaches. We took statistical analysis, a MtDeciT, in our laboratory developed tool for building decision trees with a classical method, the well-known C5.0 tool and a self-adapting evolutionary decision support model that uses evolutionary principles for the induction of decision trees. Several solutions were evolved for the classification of metabolic and respiratory acidosis (MRA). A comparison between developed models and obtained results has shown that our approach can be considered as a good choice for different kinds of real-world medical decision making.

Using intelligent search for finding medical sites.

In this paper we present an intelligent search tool, which we developed in order to automate search and evaluation of websites. We used TFIDF heuristics to determine term frequency and decision trees to evaluate the quality of sites. Training set for the decision tree contained manually evaluated websites. Each website was described by the combination of various attributes, complexity metrics and the evaluation. The intelligent search tool is equipped with a user-friendly interface, which enables people to exploit the tool to its limits with minimum effort, in their quest for information. For testing purposes, we looked for sites with telemedical content. The set of sites, which was the result of using the intelligent search tool, has been evaluated by a group of students.

The influence of class discretization to attribute hierarchy of decision trees.

Decision support systems that help physicians are becoming very important part of medical decision making. They are based on different models and the best of them are providing an explanation together with an accurate, reliable and quick response. One of the most viable among decision-making models is the concept of decision trees, already successfully used for many medical decision making purposes. Although effective and reliable, the traditional decision tree construction approach still contains several deficiencies. Therefore we decided to develop and compare several decision supporting models, each of them built with different discretization of attributes and decision classes. For the construction of decision trees we used MtDeciT, in our laboratory developed tool for building decision trees using the classical induction method. All solutions were evolved for determining the influence of basic properties of child and his/her parents to length of successful breastfeeding. A comparison between developed models and obtained results has shown that the way of discretization obviously plays a great role in the reliable and accurate real-world medical decision making.

A tutor for nursing process education.

Computer managed instruction (CMI) has been used in nursing education since the late 1960's. It is due to the accessibility and self paced format that CMI is very well suited for both students and practicing nurses, while learning can occur at the learner's own pace and time. In addition CMI supports also continuing education and distant learning. The aim of this paper is to present CArE--a software package for Computer Aided Nurse Education in particular for teaching the basics of the nursing care process, developed as a result of the Phare TEMPUS project called NICE (Nursing Informatics and Computer Aided Education).

The limitations of decision trees and automatic learning in real world medical decision making.

The decision tree approach is one of the most common approaches in automatic learning and decision making. It is popular for its simplicity in constructing, efficient use in decision making and for simple representation, which is easily understood by humans. The automatic learning of decision trees and their use usually show very good results in various "theoretical" environments. The training sets are usually large enough for learning algorithm to construct a hypothesis consistent with the underlying concept. But in real life it is often impossible to find the desired number of training objects for various reasons. The lack of possibilities to measure attribute values, high cost and complexity of such measurements, unavailability of all attributes at the same time are the typical representatives. There are different ways to deal with some of these problems, but in a delicate field of medical decision making, we cannot allow ourselves to make any inaccurate decisions. We have measured the values of 24 attributes before and after the 82 operations of children in age between 2 and 10 years. The aim was to find the dependencies between attribute values and a child's predisposition to acidemia--the decrease of blood's pH. Our main interest was in discovering predisposition to two forms of acidosis, the metabolic acidosis and the respiratory acidosis, which can both have serious effects on child's health. We decided to construct different decision trees from a set of training objects, which was complete (there were no missing attribute values), but on the other hand not large enough to avoid the effect of overfitting. A common approach to evaluation of a decision tree is the use of a test set. In our case we decided that instead of using a test set, we ask medical experts to take a closer look at the generated trees. They examined and evaluated the decision trees branch by branch. Their comments on the generated trees can be found in this paper. The comments show, that trees generated from available training set mainly have surprisingly good branches, but on the other hand some are very "stupid" and no medical explanation could be found. Thereafter we can conclude, that the decision tree concept and automatic learning can be successfully used in real world situations, constrained with the real world limitations, but they should be used only with the guidelines of appropriate medical experts.

The limitations of decision trees and automatic learning in real world medical decision making.

The decision tree approach is one of the most common approaches in automatic learning and decision making. The automatic learning of decision trees and their use usually show very good results in various "theoretical" environments. But in real life it is often impossible to find the desired number of representative training objects for various reasons. The lack of possibilities to measure attribute values, high cost and complexity of such measurements, and unavailability of all attributes at the same time are the typical representatives. For this reason we decided to use the decision trees not for their primary task--the decision making--but for outlining the most important attributes. This was possible by using a well-known property of the decision trees--their knowledge representation, which can be easily understood by humans. In a delicate field of medical decision making, we cannot allow ourselves to make any inaccurate decisions and the "tips," provided by the decision trees, can be of a great assistance. Our main interest was to discover a predisposition to two forms of acidosis: the metabolic acidosis and respiratory acidosis, which can both have serious effects on child's health. We decided to construct different decision trees from a set of training objects. Instead of using a test set for evaluation of a decision tree, we asked medical experts to take a closer look at the generated trees. They examined and evaluated the decision trees branch by branch. Their comments show that trees generated from the available training set mainly have surprisingly good branches, but on the other hand, for some, no medical explanation could be found.

Bronchoalveolar lavage in the diagnosis of peripheral primary and secondary malignant lung tumors.

The success rate of cytological examination of the bronchoalveolar lavage fluid (BALF) in patients with primary or metastatic lung tumors varies considerably according to different authors. In a prospective study, we have tried to establish the sensitivity of BAL in comparison with both transbronchial lung biopsy (TBB) and brushing. BAL was performed in 61 patients with lung malignancies and in 56 patients with nonmalignant lung disease: 39 patients had primary lung cancer, 22 had metastatic spread into the lungs. First the part of the lung involved was washed out with 100 ml physiological saline solution during bronchoscopy. Following BAL, 5 TBB and brushing were performed. The smears were stained by the May-Grünwald-Giemsa method. Malignant cells were found in BALF from 17 patients, in TBB specimens from 43 and in brushing smears from 26. TBB was significantly the most successful method applied. Malignant cells were never found in BALF only, nor were they ever found in patients with non-malignant lung disease. The sensitivity of the three methods was equal for primary as compared to metastatic tumors and for interstitial infiltrates as compared to coin lesions. Malignant cells were most frequently found in centrifuged specimens. BAL may be used in suspected malignant interstitial or rounded pulmonary infiltrates when it cannot be reached by forceps or brush, or when TBB and brushing are contra-indicated.

Human leukocyte interferon alpha (HLI-alpha) for treatment of pleural effusion caused by non small cell lung cancer. A pilot study.

Fourteen patients with ipsilateral pleural effusion from non small cell cancer of the lung, 10 of them with generalized metastasis, were treated with local application of HLI-alpha in addition to other symptomatic treatment. Cytology of pleural fluid at the beginning of treatment yielded cancer cells in all but one. HLI-alpha, 2 x 10(6) International Units (I.U.) diluted in 20 ml of distilled water was injected intrapleurally each time. The mean survival of the HLI-alpha treated patients, measured from the first treatment of the pleural effusion, was 10.8 months. The performance status improved in 9 patients following HLI-alpha treatment. The pleural effusion eventually ceased accumulating in all patients. To judge from cytology of tapped pleural fluid, the cancer cells disappeared during treatment with HLI-alpha in 11 patients.