A new scoring system in Cystic Fibrosis: statistical tools for database analysis - a preliminary report.

BACKGROUND: Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. METHODS: The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. RESULTS: (1) Feature selection: CAP has a more effective "modelling" focus than DA.(2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. CONCLUSION: Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset.

New algorithms for multi-class cancer diagnosis using tumor gene expression signatures.

MOTIVATION: The increasing use of DNA microarray-based tumor gene expression profiles for cancer diagnosis requires mathematical methods with high accuracy for solving clustering, feature selection and classification problems of gene expression data. RESULTS: New algorithms are developed for solving clustering, feature selection and classification problems of gene expression data. The clustering algorithm is based on optimization techniques and allows the calculation of clusters step-by-step. This approach allows us to find as many clusters as a data set contains with respect to some tolerance. Feature selection is crucial for a gene expression database. Our feature selection algorithm is based on calculating overlaps of different genes. The database used, contains over 16 000 genes and this number is considerably reduced by feature selection. We propose a classification algorithm where each tissue sample is considered as the center of a cluster which is a ball. The results of numerical experiments confirm that the classification algorithm in combination with the feature selection algorithm perform slightly better than the published results for multi-class classifiers based on support vector machines for this data set. AVAILABILITY: Available on request from the authors.

Using global optimization to improve classification for medical diagnosis and prognosis.

Global optimization-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with data from various databases. First, we discuss feature selection, the problem of determining the most informative features for classification in the databases under consideration. Then, we apply a technique based on convex and global optimization for classification in these databases. The third application of this technique is a method that calculates centers of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves high accuracy with these databases. Better classifiers will lead to improved assistance in making medical diagnostic and prognostic decisions.

Case-based support in a cooperative medical diagnosis environment.

Many medical services are often not available to people living in remote areas because of the lack of medical specialists. This problem would be alleviated if a suitable environment was designed to allow physicians to collaborate and exchange ideas with centrally located medical specialists. This article describes an ongoing research project to design and implement a collaborative multimedia environment to allow medical specialists to cooperate in diagnosis. The environment will support remote database access for medical images, the retrieval of relevant medical cases to support diagnosis, and communication among participants through telepointers and image annotation by free-hand drawing.

Delivery and interactive processing of visual data for a cooperative telemedicine environment.

Cooperative telemedicine environments are required for many situations such as consultations between residents and senior doctors, case correlations, and for teaching and research purposes. The mode of collaboration may vary with different situations, in terms of the synchronisation of tasks, the sharing of data and the extent of collaboration among participants. It is essential for participants to be able to remotely view and manipulate visual data (images, two-dimensional and three-dimensional graphics, animation, and video) as well as interactively run application programs that involve visual data in real-time. However, this is not possible with current network bandwidth limitations when large amount of visual data are involved. In this article, we first provide an analysis of functional requirements by participants in cooperative diagnosis in different types of situations, before discussing technical requirements, which form the basis for our system architecture design. A new approach is also presented for efficient handling of programs, which involve visual data in real time. This is achieved via the construction and transmission of small messages that encapsulate the operations in a pipelined or hierarchical fashion.

Retrieving cases for treatment advice in nursing using text representation and structured text retrieval.

A nursing database which records patient details and treatments as fields in a standard database format is transformed into a collection, in text form, of patient case days with history. Each case is represented as text strings encoding the patient details, the current problems, treatments and their associated history. The cosine measure of similarity is used to compute a whole case similarity between a text query and the cases in text form. This standard text retrieval technique is used and compared to a simple rule base. In case-based reasoning, the similarity of cases is often computed by combining similarities of the case features involved. In this work the standard text retrieval function is modified to incorporate this case structure by combining individual matches of case components based on the cosine measure. The combination is based on a linear regression model for learning the weights assigned to the components of this retrieval function. For the 1355 records two tasks were tried: predicting the treatment for a new problem and predicting the treatment for a continuing problem when a change of treatment is required. Simple text retrieval was better than the rule base for one task and case structured retrieval was at least 18% better on both tasks. Further techniques are discussed.