A database management system for cardiovascular disease

The Loyola Open-Heart Registry is a fully operational database that has hundreds of programs designed to input, modify, verify, maintain, update and analyze its raw data. It contains retrospectively collected data on approximately 4000 patients who underwent coronary bypass (CABG) or cardiac valve replacement (CVR) from January 1970 to May 1981. Since 1981 we have collected detailed information on approximately 1300 patients per year. The system is described in clinical and technical terms. The means of data acquisition and input are described and a flow chart for the collection of data is provided. The system's hardware is described briefly, and the nature and limitations of the software are discussed. Specific datasheets are described, and examples of output retrievals are provided. A few comments are made regarding the administrative aspects of database management. The entire paper is summarized, and our future plans and areas of development are described.     

The antigenic index: a novel algorithm for predicting antigenic determinants

In this paper, we introduce a computer algorithm which can be used to predict the topological features of a protein directly from its primary amino acid sequence. The computer program generates values for surface accessibility parameters and combines these values with those obtained for regional backbone flexibility and predicted secondary structure. The output of this algorithm, the antigenic index, is used to create a linear surface contour profile of the protein. Because most, if not all, antigenic sites are located within surface exposed regions of a protein, the program offers a reliable means of predicting potential antigenic determinants. We have tested the ability of this program to generate accurate surface contour profiles and predict antigenic sites from the linear amino acid sequences of well-characterized proteins and found a strong correlation between the predictions of the antigenic index and known structural and biological data.     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.     

A PC-based system for predicting movement from deep brain signals in Parkinson's disease

There is much current interest in deep brain stimulation (DBS) of the subthalamic nucleus (STN) for the treatment of Parkinson's disease (PD). This type of surgery has enabled unprecedented access to deep brain signals in the awake human. In this paper we present an easy-to-use computer based system for recording, displaying, archiving, and processing electrophysiological signals from the STN. The system was developed for predicting self-paced hand-movements in real-time via the online processing of the electrophysiological activity of the STN. It is hoped that such a computerised system might have clinical and experimental applications. For example, those sites within the STN most relevant to the processing of voluntary movement could be identified through the predictive value of their activities with respect to the timing of future movement.     

ROGET: A knowledge-based system for acquiring the conceptual structure of a diagnostic expert system

This paper describes ROGET, a knowledge-based system that assists a domain expert with an important design task encountered during the early phases of expert-system construction. ROGET conducts a dialogue with the expert to acquire the expert system's conceptual structure, a representation of the kinds of domain-specific inferences that the consultant will perform and the facts that will support these inferences. ROGET guides this dialogue on the basis of a set of advice and evidence categories. These abstract categories are domain independent and can be employed to guide initial knowledge acquisition dialogues with experts for new applications. This paper discusses the nature of an expert system's conceptual structure and describes the organization and operation of the ROGET system that supports the acquisition of conceptual structures.     

QUAWDS: a composite diagnostic system for gait analysis

QUAWDS is a system for interpreting human gait. This task is complex because patients usually have multiple faults, and there is significant interaction between the components in the system. QUAWDS interprets a gait study by integrating associational and qualitative models of knowledge into a diagnostic system, taking advantage of the subtasks each kind of model can perform efficiently and effectively. An abductive hypothesis assembler is used to coordinate the subtasks. The result is a diagnostic solution that is 'locally best', i.e., no single change to the answer will produce a better solution.     

mROC: a computer program for combining tumour markers in predicting disease states

Receiver operating characteristic (ROC) curves are limited when several diagnostic tests are available, mainly due to the problems of multiplicity and inter-relationships between the different tests. The program presented in this paper uses the generalised ROC criteria, as well as its confidence interval, obtained from the non-central F distribution, as a possible solution to this problem. This criterion corresponds to the best linear combination of the test for which the area under the ROC curve is maximal. Quantified marker values are assumed to follow a multivariate normal distribution but not necessarily with equal variances for two populations. Other options include Box-Cox variable transformations, QQ-plots, interactive graphics associated with changes in sensitivity and specificity as a function of the cut-off. We provide an example to illustrate the usefulness of data transformation and of how linear combination of markers can significantly improve discriminative power. This finding highlights potential difficulties with methods that reject individual markers based on univariate analyses.     

ANDES: a phased-array antenna diagnostic expert system

The Antenna Repair Facility at McClellan Air Force Base, which has been responsible for the repair and maintenance of US Air Force antennas and radars over several decades, now faces the challenge of transferring their many years of expertise in antenna repair and maintenance from their closing base to an acquiring base. This is being done with the requirement of maintaining the in-house expertise and production levels, while manpower decreases.

To meet the challenge, an expert system called ANDES is being developed to help diagnose electronically steered phased-array satellite ground station antennas at McClellan AFB and the acquiring base. This paper discusses the key issues in the design and development of ANDES and focuses on its use as a tool to help human engineers improve the antenna diagnostic process. The ANDES’ experience indicates that during the times of downsizing, streamlining and restructuring, expert systems offer a viable and sometimes pivotal means to preserve expertise from a closing facility and also can be used as a training tool by an acquiring facility.     

ENIGMA: a system that learns diagnostic knowledge

The results of extensive experimentation aimed at assessing the concrete possibilities of automatically building a diagnostic expert system, to be used in-field in an industrial domain, by means of machine learning techniques, are described. The system, ENIGMA, is an incremental version of the ML-SMART system, which acquires a network of first-order logic rules, starting from a set of classified examples and a domain theory. An application is described that consists of discovering malfunctions in electromechanical apparatus. ENIGMA's efficacy in acquiring sophisticated knowledge and handling complex structured examples is largely due to its underlying database management system, which supports the learning operators, defined at the abstract level, with a set of primitives, taken from the field of deductive databases. An expert system, MEPS, devoted to the same task, has also been manually developed. A number of comparisons along different dimensions of the manual and automatic development process have been possible, allowing some practical indications to be suggested     

A visualization system for space-time and multivariate patterns (VIS-STAMP)

The research reported here integrates computational, visual and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivariate, spatial and temporal dimensions via clustering, sorting and visualization. Specifically, the approach involves a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices (including several ordering methods), a geographic small multiple display and a 2-dimensional cartographic color design method. The coupling among these methods leverages their independent strengths and facilitates a visual exploration of patterns that are difficult to discover otherwise. The visualization system we developed supports overview of complex patterns and through a variety of interactions, enables users to focus on specific patterns and examine detailed views. We demonstrate the system with an application to the IEEE InfoVis 2005 contest data set, which contains time-varying, geographically referenced and multivariate data for technology companies in the US     

Decomposition of a multivariate continuous system with pureintegrations

An algorithm for extracting the integrating part of a linear multivariable continuous system is presented. It is shown that, in general, a continuous system can be additively decomposed as a subsystem with a nonsingular system matrix and a subsystem with a bank of pure integrators     

A surgical simulation system for predicting facial soft tissue deformation

Computational Visual Media - In the field of cranio-maxillofacial (CMF) surgery, surgical simulation is becoming a very powerful tool to plan surgery and simulate surgical results before actually...     

A general expert system design for diagnostic problem solving

Existing expert systems have a high percentage agreement with experts in a particular field in many situations. However, in many ways their overall behavior is not like that of a human expert. These areas include the inability to give flexible, functional explanations of their reasoning processes, and the failure to degrade gracefully when dealing with problems at the periphery of their knowledge. These two important shortcomings can be improved when the right knowledge is available to the system. This paper presents an expert system design, called the integrated diagnostic model (IDM), that integrates two sources of knowledge, a shallow, reasoning-oriented, experiential knowledge base and a deep, functionally oriented, physical knowledge base. To demonstrate the IDM's usefulness in the problem area of diagnosis and repair, an implementation in the mechanical domain is described.     

Metaheuristic optimization of multivariate adaptive regression splines for predicting the schedule of software projects

A qualitative common perception of the software industry is that it finishes its projects late and over budget, whereas from a quantitative point of view, only 39 % of software projects are finished on time compared to the schedule when the project started. This low percentage has been attributed to factors such as unrealistic time frames and lack of planning regarding poor prediction. The main techniques used for predicting project schedule have mainly been based on expert judgment and mathematical models. In this study, a new model, derived from the multivariate adaptive regression splines (MARS) model, is proposed. This new model, optimized MARS (OMARS), uses a simulated annealing process to find a transformation of the input data space prior to applying MARS in order to improve accuracy when predicting the schedule of software projects. The prediction accuracy of the OMARS model is compared to that of stand-alone MARS and a multiple linear regression (MLR) model with a logarithmic transformation. The two independent variables used for training and testing the models are functional size, which corresponds to a composite value of 19 independent variables, and the maximum size of the team of developers. The data set of projects was obtained from the International Software Benchmarking Standards Group (ISBSG) Release 11. Results based on the absolute residuals and t paired and Wilcoxon statistical tests showed that prediction accuracy with OMARS is statistically better than that with the MARS and MLR models.