uu.seUppsala University Publications
Change search
ReferencesLink to record
Permanent link

Direct link
Dynamic decision support graph: Visualization of ANN-generated diagnostic indications of pathological conditions developing over time
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
2008 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 42, no 3, 189-198 p.Article in journal (Refereed) Published
Abstract [en]

Objectives: A common objection to using artificial neural networks in clinical decision support systems is that the reasoning behind diagnostic indications cannot be sufficiently well explained. This paper presents a method for visualizing diagnostic indications generated from an artificial neural network-based decision support algorithm (ANN-algorithm) in conditions developing over time. Methods: The main idea behind the method is first to calculate and graphically present the decision regions corresponding to the diagnostic indications given as output from the ANN-algorithm, in the space of two selected, clinically established 'display variables'. Secondly, the trajectory of time series measurement results of these, often biochemical markers, together with the respective 95% confidence intervals are superimposed on the decision regions. This wilt permit a nurse or clinician to grasp the diagnostic indication graphically at a glance. The indication is further presented in relation to clinical variables that the clinician is already familiar with, thus providing a sort of explanation. The predictive value of the indication is expressed by the proximity of the measurement result to the decision boundary, separating the decision regions, and by a numerically calculated individualized predictive value. Results: The method is illustrated as applied to a previously published ANN-algorithm for the early ruling-in and ruling-out of acute myocardial infarction, using monitoring of measurement results of myoglobin and troponin-1 in plasma. Conclusion: The method is appropriate when there is a limited number of clinically established variables, i.e. variables which the clinician is used to taking into account in clinical reasoning.

Place, publisher, year, edition, pages
2008. Vol. 42, no 3, 189-198 p.
Keyword [en]
artificial neural network, visualization, acute myocardial infarction, myoglobin, troponin-1
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-110466DOI: 10.1016/j.artmed.2007.10.002ISI: 000254379400002PubMedID: 18459185OAI: oai:DiVA.org:uu-110466DiVA: diva2:277172
Available from: 2009-11-16 Created: 2009-11-16 Last updated: 2010-06-04Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed
By organisation
Department of Medical Sciences
In the same journal
Artificial Intelligence in Medicine
Medical and Health Sciences

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 147 hits
ReferencesLink to record
Permanent link

Direct link