Case-Based Explanation for Medical Diagnostic Programs, with an Example from Gynaecology

Stamper, R.; Todd, Bryan S.; Macpherson, Paul

doi:10.1055/s-0038-1635005

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…One program that has performed as well as independence Bayes is nearest neighbours with the Bayes metric. In our view, the nearest neighbours method is particularly suited to medical diagnosis because it would appear to be more accountable [75]. The diagnostic prediction of the system regarding a new case is encoded as a small set of previous actual cases.…”

Section: Resultsmentioning

confidence: 99%

The Relative Accuracy of a Variety of Medical Diagnostic Programs

Todd

Stamper²

1994

Methods Inf Med

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Abstract:Acute abdominal pain is one of the most widely studied applications of computer-aided diagnosis. The usual approach is to apply Bayes’ theorem with the assumption of conditional independence (“independence Bayes”). We compared various approaches to designing diagnostic programs for abdominal pain of suspected gynaecological origin. The methods range from statistical to knowledge-based. All programs were evaluated using a database of 1,270 cases collected retrospectively. Our results suggest that in this application no significant improvement in accuracy can be made by taking interactions into account, either by statistical or by knowledge-based means; independence Bayes is near-optimal. As far as accuracy is concerned, there appears to be little point in pursuing knowledge-based approaches. However, the “nearest neighbours” method using a new metric appears to be at least as accurate as independence Bayes. We argue that the nearest neighbours method is more suitable than independence Bayes for clinical use because of greater accountability.

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Section: Resultsmentioning

confidence: 99%

The Relative Accuracy of a Variety of Medical Diagnostic Programs

Todd

Stamper²

1994

Methods Inf Med

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“…• Stamper et al' s study describes a system which provides diagnostic assistance based on the retrieval of similar previously diagnosed cases from a database [ 4]. The aim of the study is to compare statistical approaches to retrieval, in terms of their diagnostic accuracy and also their user interface and explanation capabilities.…”

Section: Decision Support Systemsmentioning

confidence: 99%

Decision Support Systems

Heathfield¹

1995

Yearb Med Inform

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“…To quantify similarity between two cases, for each of these attributes a similarity measure has to be defined that will provide a local similarity value for the two instances of the attribute. A variety of similarity measures has been described, for example by fuzzy matching [13], cross-correlation [14], and Bayes’ theorem [15]. For this paper, it is important to distinguish between numeric and nominal value domains of attributes.…”

Section: Introductionmentioning

confidence: 99%

Methods for a similarity measure for clinical attributes based on survival data analysis

Karmen

Gietzelt

Knaup

et al. 2019

BMC Med Inform Decis Mak

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BackgroundCase-based reasoning is a proven method that relies on learned cases from the past for decision support of a new case. The accuracy of such a system depends on the applied similarity measure, which quantifies the similarity between two cases. This work proposes a collection of methods for similarity measures especially for comparison of clinical cases based on survival data, as they are available for example from clinical trials.MethodsOur approach is intended to be used in scenarios, where it is of interest to use longitudinal data, such as survival data, for a case-based reasoning approach. This might be especially important, where uncertainty about the ideal therapy decision exists. The collection of methods consists of definitions of the local similarity of nominal as well as numeric attributes, a calculation of attribute weights, a feature selection method and finally a global similarity measure. All of them use survival time (consisting of survival status and overall survival) as a reference of similarity. As a baseline, we calculate a survival function for each value of any given clinical attribute.ResultsWe define the similarity between values of the same attribute by putting the estimated survival functions in relation to each other. Finally, we quantify the similarity by determining the area between corresponding curves of survival functions. The proposed global similarity measure is designed especially for cases from randomized clinical trials or other collections of clinical data with survival information. Overall survival can be considered as an eligible and alternative solution for similarity calculations. It is especially useful, when similarity measures that depend on the classic solution-describing attribute “applied therapy” are not applicable. This is often the case for data from clinical trials containing randomized arms.ConclusionsIn silico evaluation scenarios showed that the mean accuracy of biomarker detection in k = 10 most similar cases is higher (0.909–0.998) than for competing similarity measures, such as Heterogeneous Euclidian-Overlap Metric (0.657–0.831) and Discretized Value Difference Metric (0.535–0.671). The weight calculation method showed a more than six times (6.59–6.95) higher weight for biomarker attributes over non-biomarker attributes. These results suggest that the similarity measure described here is suitable for applications based on survival data.

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Case-Based Explanation for Medical Diagnostic Programs, with an Example from Gynaecology

Cited by 10 publications

References 23 publications

The Relative Accuracy of a Variety of Medical Diagnostic Programs

The Relative Accuracy of a Variety of Medical Diagnostic Programs

Decision Support Systems

Methods for a similarity measure for clinical attributes based on survival data analysis

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