Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis.

BACKGROUND: Metabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients. RESULTS: Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS. CONCLUSION: DRSA provides a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition-decision relationships not otherwise revealed in metabolomics data.

Design and development of a mobile system for supporting emergency triage.

OBJECTIVES: Our objective was to design and develop a mobile clinical decision support system for emergency triage of different acute pain presentations. The system should interact with existing hospital information systems, run on mobile computing devices (handheld computers) and be suitable for operation in weak-connectivity conditions (with unstable connections between mobile clients and a server). METHODS: The MET (Mobile Emergency Triage) system was designed following an extended client-server architecture. The client component, responsible for triage decision support, is built as a knowledge-based system, with domain ontology separated from generic problem solving methods and used for the automatic creation of a user interface. RESULTS: The MET system is well suited for operation in the Emergency Department of a hospital. The system's external interactions are managed by the server, while the MET clients, running on handheld computers are used by clinicians for collecting clinical data and supporting triage at the bedside. The functionality of the MET client is distributed into specialized modules, responsible for triaging specific types of acute pain presentations. The modules are stored on the server, and on request they can be transferred and executed on the mobile clients. The modular design provides for easy extension of the system's functionality. A clinical trial of the MET system validated the appropriateness of the system's design, and proved the usefulness and acceptance of the system in clinical practice. CONCLUSIONS: The MET system captures the necessary hospital data, allows for entry of patient information, and provides triage support. By operating on handheld computers, it fits into the regular emergency department workflow without introducing any hindrances or disruptions. It supports triage anytime and anywhere, directly at the point of care, and also can be used as an electronic patient chart, facilitating structured data collection.

Intelligent decision support in pathomorphology.

This paper presents a novel approach to computer-supported diagnosing based on microscopic images of histological sections. A method of extraction of textural feature is presented, which is in a sense complementary to the texture-based segmentation. The textural feature is obtained by tracing the process of image segmentation. For classification, a n2-classifier oriented to multi-class problems has been used. The paper presents also an empirical verification of the proposed approach on 700 microscopic images representing 14 classes of CNS neuroepithelial tumours, in which case an encouraging accuracy of classification on the testing set (70.6%) has been obtained.

Learning the preferences of physicians for the organization of result lists of medical evidence articles.

BACKGROUND: Online medical knowledge repositories such as MEDLINE and The Cochrane Library are increasingly used by physicians to retrieve articles to aid with clinical decision making. The prevailing approach for organizing retrieved articles is in the form of a rank-ordered list, with the assumption that the higher an article is presented on a list, the more relevant it is. OBJECTIVES: Despite this common list-based organization, it is seldom studied how physicians perceive the association between the relevance of articles and the order in which articles are presented. In this paper we describe a case study that captured physician preferences for 3-element lists of medical articles in order to learn how to organize medical knowledge for decision-making. METHODS: Comprehensive relevance evaluations were developed to represent 3-element lists of hypothetical articles that may be retrieved from an online medical knowledge source such as MEDLINE or The Cochrane Library. Comprehensive relevance evaluations asses not only an article's relevance for a query, but also whether it has been placed on the correct list position. In other words an article may be relevant and correctly placed on a result list (e.g. the most relevant article appears first in the result list), an article may be relevant for a query but placed on an incorrect list position (e.g. the most relevant article appears second in a result list), or an article may be irrelevant for a query yet still appear in the result list. The relevance evaluations were presented to six senior physicians who were asked to express their preferences for an article's relevance and its position on a list by pairwise comparisons representing different combinations of 3-element lists. The elicited preferences were assessed using a novel GRIP (Generalized Regression with Intensities of Preference) method and represented as an additive value function. Value functions were derived for individual physicians as well as the group of physicians. RESULTS: The results show that physicians assign significant value to the 1st position on a list and they expect that the most relevant article is presented first. Whilst physicians still prefer obtaining a correctly placed article on position 2, they are also quite satisfied with misplaced relevant article. Low consideration of the 3rd position was uniformly confirmed. CONCLUSIONS: Our findings confirm the importance of placing the most relevant article on the 1st position on a list and the importance paid to position on a list significantly diminishes after the 2nd position. The derived value functions may be used by developers of clinical decision support applications to decide how best to organize medical knowledge for decision making and to create personalized evaluation measures that can augment typical measures used to evaluate information retrieval systems.

Rough sets approach to analysis of data from peritoneal lavage in acute pancreatitis.

Two kinds of information systems composed of data from peritoneal lavage in acute pancreatitis are analysed with the concept of rough sets: system A, classifying patients described by pre-lavage attributes, and system B, classifying patients described by attributes of the course of the multistage lavage. The analysis tends to define subsets which are significant for high quality of classification. These attributes give the best description of the patient's state and are in the closest relationship with the time of lavage. The character of this relationship is shown by decision algorithms derived from decision tables representing the information system.

Triage of the child with abdominal pain: A clinical algorithm for emergency patient management.

OBJECTIVE: To create a simplified clinical algorithm for the triage of children with abdominal pain. DESIGN: Retrospective analysis. SETTING: Emergency room at the Children's Hospital of Eastern Ontario, Ottawa, Ontario. METHODS: A data mining methodology (rough sets analysis) was applied to a randomized data set obtained from 175 emergency room admission charts of patients. Patients were placed into two diagnostic decision classes: appendicitis confirmed by a pathological report, and resolution (this classification implied the resolution of all clinical complaints and physical findings, with no pathological diagnosis and no operative procedure). RESULTS: Nine clinical symptoms and signs were identified as being important in the management of children with abdominal pain. A clinically based algorithm for the triage of such children was developed. CONCLUSIONS: It is possible to develop a clinical algorithm for the triage of children with abdominal pain that can also be used by nonmedical professionals. A template for such an algorithm can be used as the basis for diagnosing other paediatric emergencies, such as chest pain, headaches and joint pain.