| 1. |
- Agell, Jonas, et al.
(författare)
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Growth effects of government expenditure and taxation in rich countries : A comment
- 2006
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Ingår i: European Economic Review. - : Elsevier BV. - 0014-2921 .- 1873-572X. ; 50:1, s. 211-218
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Tidskriftsartikel (refereegranskat)abstract
- Fölster and Henrekson (European Economic Review 45 (2001), 1501–1520) argue that “...the more the econometric problems that are addressed, the more robust the relationship between government size and economic growth appears”. But in failing to control for simultaneity and in ignoring issues of sample-selection bias, the regressions reported by Fölster/Henrekson are flawed. Using theoretically valid instruments, we find that the estimated partial correlation between size of the public sector and economic growth is statistically insignificant and highly unstable across specifications. Moreover, since instruments are weak, all hypothesis tests are unreliable. We conclude that cross-country growth regressions are unlikely to come up with a reliable answer to the question of the growth effects of government spending and taxation.
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| 2. |
- Björk, Jonas, et al.
(författare)
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A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department
- 2006
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Ingår i: BMC Medical Informatics and Decision Making. - : Springer Science and Business Media LLC. - 1472-6947. ; 6:28
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Tidskriftsartikel (refereegranskat)abstract
- Background Several models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a simple statistical model for ACS prediction that could be used in routine care at a busy ED. Methods Multivariable analysis and logistic regression were used on data from 634 ED visits for chest pain. Only data immediately available at patient presentation were used. To make ACS prediction stable and the model useful for personnel inexperienced in electrocardiogram (ECG) reading, simple ECG data suitable for computerized reading were included. Results Besides ECG, eight variables were found to be important for ACS prediction, and included in the model: age, chest discomfort at presentation, symptom duration and previous hypertension, angina pectoris, AMI, congestive heart failure or PCI/CABG. At an ACS prevalence of 21% and a set sensitivity of 95%, the negative predictive value of the model was 96%. Conclusions The present prediction model, combined with the clinical judgment of ED personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.
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| 3. |
- Forberg, Jakob L, et al.
(författare)
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In search of the best method to predict acute coronary syndrome using only the electrocardiogram from the emergency department.
- 2009
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Ingår i: Journal of Electrocardiology. - : Elsevier BV. - 1532-8430 .- 0022-0736. ; 42:1, s. 58-63
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: The aim of this study was to compare different methods to predict acute coronary syndrome (ACS) using only data from a single electrocardiogram (ECG) in the emergency department (ED). METHOD: We compared the ACS prediction abilities of classical ECG criteria, human expert ECG interpretation, a logistic regression model and an artificial neural network ensemble (ANN). The ED ECG and discharge diagnoses were retrieved for 861 patient visits to the ED for chest pain. Cross-validation was used to estimate the generalization performance of the logistic regression and the ANN model. RESULTS: The logistic regression model had the overall best performance in predicting ACS with an area under the receiver operating characteristic curve of 0.88. The sensitivities of logistic regression, ANN, expert physicians, and classical ECG criteria were 95%, 95%, 82%, and 75%, respectively, and the specificities were 54%, 44%, 63%, and 69%. CONCLUSION: Our logistic regression model was the best overall method to predict ACS, followed by our ANN. Decision support models have the potential to improve even experienced ECG readers' ability to predict ACS in the ED.
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| 4. |
- Fröberg, Linus, et al.
(författare)
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Transients in the Formation of Nanowire Heterostructures.
- 2008
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Ingår i: Nano Letters. - : American Chemical Society (ACS). - 1530-6992 .- 1530-6984. ; 8:11, s. 3815-3818
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Tidskriftsartikel (refereegranskat)abstract
- We present results on the effect of seed particle reconfiguration on the growth of short InAs and InP nanowire segments. The reconfiguration originates in two different steady state alloy compositions of the Au/In seed particle during growth of InAs and InP. From compositional analysis of the seed particle, the In content in the seed particle is determined to be 34 and 44% during InAs and InP growth, respectively. When switching between growing InAs and InP, transient effects dominate during the time period of seed particle reconfiguration. We developed a model that quantitatively explains the effect and with the added understanding we are now able to grow short period (<10 nm) nanowire superlattices.
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| 5. |
- Fröberg, Linus, et al.
(författare)
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Vertical InAs nanowire wrap-gate FETs
- 2006
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Ingår i: Book of abstracts: Semicond Nanowires Symp, Eindhoven, The Netherlands (2006).
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Konferensbidrag (refereegranskat)
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| 6. |
- Green, Michael, et al.
(författare)
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Best leads in the standard electrocardiogram for the emergency detection of acute coronary syndrome.
- 2007
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Ingår i: Journal of Electrocardiology. - : Elsevier BV. - 1532-8430 .- 0022-0736. ; 40:3, s. 251-256
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Tidskriftsartikel (refereegranskat)abstract
- Background and Purpose: The purpose of this study was to determine which leads in the standard 12-lead electrocardiogram (ECG) are the best for detecting acute coronary syndrome (ACS) among chest pain patients in the emergency department. Methods: Neural network classifiers were used to determine the predictive capability of individual leads and combinations of leads from 862 ECCs from chest pain patients in the emergency department at Lund University Hospital. Results: The best individual lead was aVL, with an area under the receiver operating characteristic curve of 75.5%. The best 3-lead combination was III, aVL, and V-2, with a receiver operating characteristic area of 82.0%, compared with the 12-lead ECG performance of 80.5%. Conclusions: Our results indicate that leads III, aVL, and V2 are sufficient for computerized prediction of ACS. The present results are likely important in situations where the 12-lead ECG is impractical and for the creation of clinical decision support systems for ECG prediction of ACS.
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| 7. |
- Green, Michael, et al.
(författare)
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Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room
- 2006
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 1873-2860 .- 0933-3657. ; 38:3, s. 305-318
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Tidskriftsartikel (refereegranskat)abstract
- Summary Objective Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. Methods and materials Artificial neural network (ANN) ensembles and logistic regression models were trained on data from 634 patients presenting an emergency department with chest pain. Only data immediately available at patient presentation were used, including electrocardiogram (ECG) data. The models were analyzed using receiver operating characteristics (ROC) curve analysis, calibration assessments, inter- and intra-method variations. Effective odds ratios for the ANN ensembles were compared with the odds ratios obtained from the logistic model. Results The ANN ensemble approach together with ECG data preprocessed using principal component analysis resulted in an area under the ROC curve of 80%. At the sensitivity of 95% the specificity was 41%, corresponding to a negative predictive value of 97%, given the ACS prevalence of 21%. Adding clinical data available at presentation did not improve the ANN ensemble performance. Using the area under the ROC curve and model calibration as measures of performance we found an advantage using the ANN ensemble models compared to the logistic regression models. Conclusion Clinically, a prediction model of the present type, combined with the judgment of trained emergency department personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.
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| 8. |
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| 9. |
- Green, Michael, et al.
(författare)
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Explaining artificial neural network ensembles: A case study with electrocardiograms from chest pain patients
- 2008
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Ingår i: Proceedings of the ICML/UAI/COLT 2008 Workshop on Machine Learning for Health-Care Applications.
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Konferensbidrag (refereegranskat)abstract
- Artificial neural networks is one of the most commonly used machine learning algorithms in medical applications. However, they are still not used in practice in the clinics partly due to their lack of explanatory capacity. We compare two case-based explanation methods to two trained physicians on analysis of electrocardiogram (ECG) data from patients with a suspected acute coronary syndrome (ACS). The median overlaps of the top 5 selected features between the two physicians, and a given physician and a method, were initially low. Using a correlation analysis of the features the median overlap increased to values typically in the range 3-4. In conclusion, both our case-based methods generate explanations similar to those of trained expert physicians on the problem of diagnosing ACS from ECG data.
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| 10. |
- Green, Michael, et al.
(författare)
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Exploring new possibilities for case based explanation of artificial neural network ensembles
- 2009
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Ingår i: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 22:1, s. 75-81
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Tidskriftsartikel (refereegranskat)abstract
- Artificial neural network (ANN) ensembles have long suffered from a lack of interpretability. This has severely limited the practical usability of ANNs in settings where an erroneous decision can be disastrous. Several attempts have been made to alleviate this problem. Many of them are based on decomposing the decision boundary of the ANN into a set of rules. We explore and compare a set of new methods for this explanation process on two artificial data sets (Monks 1 and 3), and one acute coronary syndrome data set consisting of 861 electrocardiograms (ECG) collected retrospectively at the emergency department at Lund University Hospital. The algorithms managed to extract good explanations in more than 84% of the cases. More to the point, the best method provided 99% and 91% good explanations in Monks data 1 and 3 respectively. Also there was a significant overlap between the algorithms. Furthermore, when explaining a given ECG, the overlap between this method and one of the physicians was the same as the one between the two physicians in this study. Still the physicians were significantly, p-value <0.001, more similar to each other than to any of the methods. The algorithms have the potential to be used as an explanatory aid when using ANN ensembles in clinical decision support systems.
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