Biomedical subjects

C D Nugent

Publications and source records attributed to C D Nugent.

6 recordsLinked to original sources

PubMed2003

Evaluation of inherent performance of intelligent medical decision support systems: utilising neural networks as an example.

Researchers who design intelligent systems for medical decision support, are aware of the need for response to real clinical issues, in particular the need to address the specific ethical problems that the medical domain has in using black boxes. This means such intelligent systems have to be thoroughly evaluated, for acceptability. Attempts at compliance, however, are hampered by lack of guidelines. This paper addresses the issue of inherent performance evaluation, which researchers have addressed in part, but a Medline search, using neural networks as an example of intelligent systems, indicated that only about 12.5% evaluated inherent performance adequately. This paper aims to address this issue by concentrating on the possible evaluation methodology, giving a framework and specific suggestions for each type of classification problem. This should allow the developers of intelligent systems to produce evidence of a sufficiency of output performance evaluation.

Artificial Intelligence↗

PubMed2002

Implementation of intelligent decision support systems in health care.

The full implementation of any intelligent system in health care, which is designed for decision support, has several stages, from initial problem identification through development and, finally, cost-benefit analysis. Central to this is formal objectivist evaluation with its core component of inherent performance of the outputs from these systems. A Medline survey of one type of intelligent system is presented, which demonstrates that this issue is not being addressed adequately. Lack of criteria for dealing with the outputs from these "black box" systems to prescribe adequate levels of inherent performance may be preventing their being accepted by those in the health-care domain and, thus, their being applied widely in the field.

Decision Support Systems, Clinical↗

PubMed2002

Performance evaluation of artificial intelligence classifiers for the medical domain.

The application of artificial intelligence systems is still not widespread in the medical field, however there is an increasing necessity for these to handle the surfeit of information available. One drawback to their implementation is the lack of criteria or guidelines for the evaluation of these systems. This is the primary issue in their acceptability to clinicians, who require them for decision support and therefore need evidence that these systems meet the special safety-critical requirements of the domain. This paper shows evidence that the most prevalent form of intelligent system, neural networks, is generally not being evaluated rigorously regarding classification precision. A taxonomy of the types of evaluation tests that can be carried out, to gauge inherent performance of the outputs of intelligent systems has been assembled, and the results of this presented in a clear and concise form, which should be applicable to all intelligent classifiers for medicine.

Artificial Intelligence↗

PubMed1999

An intelligent framework for the classification of the 12-lead ECG.

An intelligent framework has been proposed to classify an unknown 12-Lead electrocardiogram into one of a possible number of mutually exclusive and combined diagnostic classes. The framework segregates the classification problem into a number of bi-dimensional classification problems, requiring individual bi-group classifiers for each individual diagnostic class. The bi-group classifiers were generated employing Neural Networks (NN), combined with a combination framework containing an Evidential Reasoning framework to accommodate for any conflicting situations between the bi-group classifiers. A number of different feature selection techniques were investigated with the aim of generating the most appropriate input vector for the bi-group classifiers. It was found that by reducing the original input feature vector, the generalisation ability of the classifiers, when exposed to unseen data, was enhanced and subsequently this reduced the computational requirements of the network itself. The entire framework was compared with a conventional approach to NN classification and a rule based classification approach. The framework attained a significantly higher level of classification in comparison with the other methods; 80.0% compared with 66.7% for the rule based technique and 68.00% for the conventional neural approach.

Computer Simulation↗

PubMed1998

Computerised electrocardiology employing bi-group neural networks.

A configuration of bi-group neural networks (BGNN) is proposed combined with an evidential reasoning framework to interpret 12-lead electrocardiograms for three mutually exclusive classes. A number of pre-processing feature selection techniques were investigated prior to application of the input feature vector to each individual BGNN. The network outputs were discounted within a belief interval of 1 based on their performance on test data prior to combination. It was found that the application of the feature selection techniques enhanced the individual performance of the BGNN, and subsequently enhanced the overall performance. The proposed framework was compared with conventional classification techniques of multi-output neural networks and linear multiple regression. The framework attained a higher level of classification in comparison with the other methods; 70.4% compared with 66.7% for both multi-output neural and statistical techniques.

Diagnosis, Differential↗

PubMedDate not supplied

Feature and classifier fusion for 12-lead ECG classification.

Two methodologies, feature and classifier fusion, for the problem of computerized 12-lead electrocardiogram classification, are investigated. Firstly, the entire classification problem is subdivided into a number of smaller bi-dimensional ones. By employing bi-group Neural Network classifiers, independent feature vectors for each diagnostic class are examined individually and the output from each classifier are fused together to produce one single result. Secondly, two classifiers, namely the aforementioned and a decision tree, are fused together through a novel approach of a Specificity Matrix. This methodology addresses the problem of unresolved conflict during fusion of classifiers and aims to exploit the merits of each classifier and suppress their weaknesses. 290 validated 12-lead electrocardiogram recordings, comprising six diagnostic classes, were used to train, validate and test both methodologies. The framework of bi-group classifiers enhanced the overall performance by 12.0% in comparison with conventional approaches. In the second instance, the fusion of the two classifiers produced a performance level of 81.3%; superior to either classifier in isolation. This approach offers a viable solution to the unresolved problem of conflict between classifiers during fusion and can be extended readily to accommodate any number of diagnostic classes and classifiers.

Decision Trees↗