| Dynamic decision support graph--visualization of ANN-generated diagnostic indications of pathological conditions developing over time. | |
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MedLine Citation:
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PMID: 18459185 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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 will 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-I 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. |
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Authors:
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Johan Ellenius; Torgny Groth |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Artificial intelligence in medicine Volume: 42 ISSN: 0933-3657 ISO Abbreviation: Artif Intell Med Publication Date: 2008 Mar |
Date Detail:
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Created Date: 2008-05-05 Completed Date: 2008-05-29 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8915031 Medline TA: Artif Intell Med Country: Netherlands |
Other Details:
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Languages: eng Pagination: 189-98 Citation Subset: IM |
Affiliation:
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Department of LIME (Learning, Informatics, Management and Ethics), Karolinska Institutet, 171 77 Stockholm, Sweden. Johan.Ellenius@ki.se |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Angina Pectoris / blood, etiology* Artificial Intelligence* Biological Markers / blood Computer Graphics* Confidence Intervals Decision Support Systems, Clinical* Decision Support Techniques* Diagnosis, Computer-Assisted* Disease Progression Electrocardiography Female Humans Male Models, Biological Myocardial Infarction / blood, complications, diagnosis* Myoglobin / blood Neural Networks (Computer)* Predictive Value of Tests Sensitivity and Specificity Time Factors Troponin I / blood |
| Chemical | |
Reg. No./Substance:
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0/Biological Markers; 0/Myoglobin; 0/Troponin I |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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