Chi-square-based scoring function for categorization of MEDLINE citations.

OBJECTIVES: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. METHODS: Our procedure requires construction of a genetic and a nongenetic domain document corpus. We used MeSH descriptors assigned to MEDLINE citations for this categorization task. We compared frequencies of MeSH descriptors between two corpora applying chi-square test. A MeSH descriptor was considered to be a positive indicator if its relative observed frequency in the genetic domain corpus was greater than its relative observed frequency in the nongenetic domain corpus. The output of the proposed method is a list of scores for all the citations, with the highest score given to those citations containing MeSH descriptors typical for the genetic domain. RESULTS: Validation was done on a set of 734 manually annotated MEDLINE citations. It achieved predictive accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method by comparing it to three machine-learning algorithms (support vector machines, decision trees, naïve Bayes). Although the differences were not statistically significantly different, results showed that our chi-square scoring performs as good as compared machine-learning algorithms. CONCLUSIONS: We suggest that the chi-square scoring is an effective solution to help categorize MEDLINE citations. The algorithm is implemented in the BITOLA literature-based discovery support system as a preprocessor for gene symbol disambiguation process.

Diagnostic test for Y chromosome microdeletion screening in male infertility.

Despite the current lack of understanding the mechanism of deleterious effects of Y chromosome microdeletions and their prognostic influence on male subfertility, the Y chromosome microdeletion test is widely used in the diagnostic evaluation of male subfertility. However, currently used diagnostic schemes have not been sufficiently evaluated for their diagnostic performance. The purpose of this study was to analyze a large database of published Y chromosome microdeletions to develop the optimal screening strategy for male subfertility. Therefore, we created a database from genetic and clinical data published in 52 peer-reviewed studies reporting on 512 cases with Y chromosome microdeletions. We developed a computerized procedure with the goal of minimizing the number of genetic markers included in the diagnostic set while maximizing the detection rate in patients with microdeletions. We estimate that 85.6% of all published Y chromosome microdeletions can be covered by a set of six genetic markers (sY84, sY127, sY152, RBMY1, sY147, sY254-DAZ). Inclusion of additional markers brings relatively little to the sensitivity of the test and is potentially related to the population origin.

Supporting discovery in medicine by association rule mining in Medline and UMLS.

The paper presents an interactive discovery support system for the field of medicine. The intended users of the system are medical researchers. The goal of the system is: for a given starting concept of interest, discover new, potentially meaningful relations with other concepts that have not been published in the medical literature before. The known relations between the medical concepts come from the Medline bibliographic database and the UMLS. We use association rules for discovering the relationship between medical concepts. We evaluated the system by testing how successfully it predicted future discoveries (new relations between concepts). We first divided the Medline database into two segments (older and newer) using the publication date. Then we calculated how many of the new relations found by the system in the older segment become known relations in the newer segment. We found out with statistical significance that the system predicts new relations better then someone predicting randomly. The evaluation showed that our approach for supporting discovery in medicine is successful, but also that some improvements are needed, especially on limiting the number of potential discoveries the system generates.

Using data mining and OLAP to discover patterns in a database of patients with Y-chromosome deletions.

The paper presents a database of published Y chromosome deletions and the results of analyzing the database with data mining techniques. The database describes 382 patients for which 177 different markers were tested: 364 of the 382 patients had deletions. Two data mining techniques, clustering and decision tree induction were used. Clustering was used to group patients according to the overall presence/absence of deletions at the tested markers. Decision trees and On-Line-Analytical-Processing (OLAP) were used to inspect the resulting clustering and look for correlations between deletion patterns, populations and the clinical picture of infertility. The results of the analysis indicate that there are correlations between deletion patterns and patient populations, as well as clinical phenotype severity.

Using data warehousing and OLAP in public health care.

The paper describes the possibilities of using data warehousing and OLAP technologies in public health care in general and then our own experience with these technologies gained during the implementation of a data warehouse of outpatient data at the national level. Such a data warehouse serves as a basis for advanced decision support systems based on statistical, OLAP or data mining methods. We used OLAP to enable interactive exploration and analysis of the data. We found out that data warehousing and OLAP are suitable for the domain of public health and that they enable new analytical possibilities in addition to the traditional statistical approaches.

A data mining approach to the development of a diagnostic test for male infertility.

The paper presents a database of published Y chromosome deletions and the results of analyzing the database with data mining and other heuristic techniques with the goal of developing a diagnostic test for male infertility. The database describes 382 patients for which 177 markers were tested. Two data mining techniques, clustering and decision tree induction were used, as well as a heuristic set cover algorithm. Clustering was used to group markers according to their appearance across patients, while a heuristic set covering algorithm was used to select as small a set of markers that cover as many patients with deletions as possible. This algorithm created a diagnostic set of 13 markers that cover more than 90% of the patients with deletions. Finally, decision tree induction was used to relate deletion patterns to the severity of the clinical phenotype. A decision tree induced from the data uses 5 markers, all of which are also in the diagnostic set of 13 markers, to show relations between the severity of the clinical phenotype and deletion patterns which have not been known previously.

WWW search engine for Slovenian and English medical documents.

The information tool for the organization and searching of Slovenian and English medical documents is presented. The tool, partly still in development phases, performs automatic subject description of documents, searching with natural language queries and rankig of search hits according to their relevance. The search engine allows the searcher to use relevance feedback in order to perform incremental improvement of search results. The machine learning system TILDE for learning user profiles was also applied. Documents marked by the user as relevant or non-relevant are used to find characteristics that distinguish relevant documents from non-relevant ones.

Lymphocyte changes after long-term therapy of some neurological diseases.

Hematologic investigations were carried out in 50 patients with various forms of epilepsy, aged 20 to 50 years, who had been treated for different periods of time with antiepileptic and neuroleptic drugs such as primidon, luminal and prasine. All the cytomorphological and cytochemical studies concerned the white blood cells, especially lymphocytes, and their transformation into plasma cells and histiomonocytes. The cytochemical investigations referred particularly to nucleolar-RNA, glycogen and lipid cellular content. The results obtained showed that the lymphocyte count and mostly that of the large and medium lymphocytes, may reach twice the normal or more. The transition forms towards histiomonocytes and plasma cells were quite frequent. Cytochemically, a variable state of reactivity of the nucleolar apparatus was demonstrated, as well as certain changes of the carbohydrate and lipid metabolism. The influence of such changes on the cellular and humoral immunity of the respective patients is discussed. Emphasis is also laid on the utility of such investigations for the detection of the untoward effects of antiepileptic and neuroleptic drugs on hematopoiesis.