Document Detail


Dynamic decision support graph--visualization of ANN-generated diagnostic indications of pathological conditions developing over time.
MedLine Citation:
PMID:  18459185     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Johan Ellenius; Torgny Groth
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Artificial intelligence in medicine     Volume:  42     ISSN:  0933-3657     ISO Abbreviation:  Artif Intell Med     Publication Date:  2008 Mar 
Date Detail:
Created Date:  2008-05-05     Completed Date:  2008-05-29     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8915031     Medline TA:  Artif Intell Med     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  189-98     Citation Subset:  IM    
Affiliation:
Department of LIME (Learning, Informatics, Management and Ethics), Karolinska Institutet, 171 77 Stockholm, Sweden. Johan.Ellenius@ki.se
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MeSH Terms
Descriptor/Qualifier:
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:
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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