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- Ahmed, Mobyen Uddin, et al.
(författare)
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A fuzzy rule-based decision support system for Duodopa treatment in Parkinson
- 2006
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Ingår i: 23rd annual workshop of the Swedish Artificial Intelligence Society. - Umeå.
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Konferensbidrag (refereegranskat)abstract
- A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.
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| 4. |
- Eggers, Kai, et al.
(författare)
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Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients
- 2007
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Ingår i: Int J Cardiol. - : Elsevier BV. - 1874-1754 .- 0167-5273. ; 114:3, s. 366-74
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: To prospectively validate artificial neural network (ANN)-algorithms for early diagnosis of myocardial infarction (AMI) and prediction of 'major infarct' size in patients with chest pain and without ECG changes diagnostic for AMI. METHODS: Results of early and frequent Stratus CS measurements of troponin I (TnI) and myoglobin in 310 patients were used to validate four prespecified ANN-algorithms with use of cross-validation techniques. Two separate biochemical criteria for diagnosis of AMI were applied: TnI > or = 0.1 microg/L within 24 h ('TnI 0.1 AMI') and TnI > or = 0.4 microg/L within 24 h ('TnI 0.4 AMI'). To be considered clinically useful, the ANN-indications of AMI had to achieve a predefined positive predictive value (PPV) > or = 78% and a negative predictive value (NPV) > or = 94% at 2 h after admission. 'Major infarct' size was defined by peak levels of CK-MB within 24 h. RESULTS: For the best performing ANN-algorithms, the PPV and NPV for the indication of 'TnI 0.1 AMI' were 87% (p=0.009) and 99% (p=0.0001) at 2 h, respectively. For the indication of 'TnI 0.4 AMI', the PPV and NPV were 90% (p=0.006) and 99% (p=0.0004), respectively. Another ANN-algorithm predicted 'major AMI' at 2 h with a sensitivity of 96% and a specificity of 78%. Corresponding PPV and NPV were 73% and 97%, respectively. CONCLUSIONS: Specially designed ANN-algorithms allow diagnosis of AMI within 2 h of monitoring. These algorithms also allow early prediction of 'major AMI' size and could thus, be used as a valuable instrument for rapid assessment of chest pain patients.
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- Ellenius, Johan, et al.
(författare)
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Dynamic decision support graph : Visualization of ANN-generated diagnostic indications of pathological conditions developing over time
- 2008
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 0933-3657 .- 1873-2860. ; 42:3, s. 189-198
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Ellenius, Johan, et al.
(författare)
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Methods for selection of adequate neural network structures with application to early assessment of chest pain patients by biochemical monitoring
- 2000
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Ingår i: International Journal of Medical Informatics. - 1386-5056 .- 1872-8243. ; 57:2-3, s. 181-202
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Tidskriftsartikel (refereegranskat)abstract
- A methodology for selecting, training and estimating the performance of adequate artificial neural network (ANN) structures and incorporating them with algorithms that are optimized for clinical decision making is presented. The methodology was applied to the problem of early ruling-in/ruling-out of patients with suspected acute myocardial infarction using frequent biochemical monitoring. The selection of adequate ANN structures from a set of candidates was based on criteria for model compatibility, parameter identifiability and diagnostic performance. The candidate ANN structures evaluated were the single-layer perceptron (SLP), the fuzzified SLP, the multiple SLP, the gated multiple SLP, the multi-layer perceptron (MLP) and the discrete-time recursive neural network. The identifiability of the ANNs was assessed in terms of the conditioning of the Hessian of the objective function, and variability of parameter estimates and decision boundaries in the trials of leave-one-out cross-validation. The commonly used MLP was shown to be non-identifiable for the present problem and available amount of data, despite artificially reducing the model complexity with use of regularization methods. The investigation is concluded by recommending a number of guidelines in order to obtain an adequate ANN model.
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| 8. |
- Ellenius, Johan, et al.
(författare)
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Transferability of neural network-based decision support algorithms for early assessment of chest-pain patients
- 2000
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Ingår i: International Journal of Medical Informatics. - 1386-5056 .- 1872-8243. ; 60:1, s. 1-20
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Tidskriftsartikel (refereegranskat)abstract
- The present investigation concerns methodological and epidemiological aspects of the transferability of artificial neural network-based algorithms, as key-components for classification in decision support systems (DSS). The prevalence of pathological conditions to be detected must be known in order to tune an artificial neural networks (ANN)-decision algorithm so that the predictive values of the outcome fulfil medical requirements. Another aspect of transferability, when clinical laboratory results are used, concerns differences in analytical performance of measuring instruments. The relative bias between two instruments is not known exactly, but must be estimated and corrected for. A general method, based on original measured data sets and statistical modeling, was developed for simulating the impact of various correction procedures when using different analytical instruments. The simulation methodology was applied to a real clinical problem of ruling-in/ruling-out of patients with suspected acute myocardial infarction (AMI) by biochemical monitoring. The recommended correction procedure was based on method comparison with use of five duplicate measurements on a common set of patient samples covering the relevant measuring interval. Transferability of laboratory data over time was also studied. The design of quality assurance procedures should be based on analytical quality requirement specifications related to medical needs. Limits of critically sized systematic errors were assessed by calculating the decrease in diagnostic performance of the ANN-algorithm as a result of temporary analytical disturbances. The consequences for the design of QA procedures was illustrated. It is concluded that the actual ANN-decision algorithm for early assessment of chest-pain patients should be possible to transfer to new sites under realistic conditions.
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