| 1. |
- 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. - BioMed Central. - 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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| 2. |
- Björkqvist, Maria, et al.
(författare)
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Evaluation of a previously suggested plasma biomarker panel to identify Alzheimer's disease.
- 2012
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Ingår i: PloS one. - Public Library of Science. - 1932-6203. ; 7:1, s. e29868
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Tidskriftsartikel (refereegranskat)abstract
- There is an urgent need for biomarkers in plasma to identify Alzheimer's disease (AD). It has previously been shown that a signature of 18 plasma proteins can identify AD during pre-dementia and dementia stages (Ray et al, Nature Medicine, 2007). We quantified the same 18 proteins in plasma from 174 controls, 142 patients with AD, and 88 patients with other dementias. Only three of these proteins (EGF, PDG-BB and MIP-1δ) differed significantly in plasma between controls and AD. The 18 proteins could classify patients with AD from controls with low diagnostic precision (area under the ROC curve was 63%). Moreover, they could not distinguish AD from other dementias. In conclusion, independent validation of results is important in explorative biomarker studies.
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| 3. |
- Blankenbecler, R, et al.
(författare)
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Matching protein structures with fuzzy alignments
- 2003
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - National Academy of Sciences U S A. - 0027-8424. ; 100:21, s. 11936-11940
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Tidskriftsartikel (refereegranskat)abstract
- Unraveling functional and ancestral relationships between proteins as well as structure-prediction procedures require powerful protein-alignment methods. A structure-alignment method is presented where the problem is mapped onto a cost function containing both fuzzy (Potts) assignment variables and atomic coordinates. The cost function is minimized by using an iterative scheme, where at each step mean field theory methods at finite "temperatures" are used for determining fuzzy assignment variables followed by exact translation and rotation of atomic coordinates weighted by their corresponding fuzzy assignment variables. The approach performs very well when compared with other methods, requires modest central processing unit consumption, and is robust with respect to choice of iteration parameters for a wide range of proteins.
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| 4. |
- Carlsson, Anders, et al.
(författare)
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Molecular serum portraits in patients with primary breast cancer predict the development of distant metastases.
- 2011
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - 1091-6490. ; 108:34, s. 14252-14257
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Tidskriftsartikel (refereegranskat)abstract
- The risk of distant recurrence in breast cancer patients is difficult to assess with current clinical and histopathological parameters, and no validated serum biomarkers currently exist. Using a recently developed recombinant antibody microarray platform containing 135 antibodies against 65 mainly immunoregulatory proteins, we screened 240 sera from 64 patients with primary breast cancer. This unique longitudinal sample material was collected from each patient between 0 and 36 mo after the primary operation. The velocity for each serum protein was determined by comparing the samples collected at the primary operation and then 3-6 mo later. A 21-protein signature was identified, using leave-one-out cross-validation together with a backward elimination strategy in a training cohort. This signature was tested and evaluated subsequently in an independent test cohort (prevalidation). The risk of developing distant recurrence after primary operation could be assessed for each patient, using her molecular portraits. The results from this prevalidation study showed that patients could be classified into high- versus low-risk groups for developing metastatic breast cancer with a receiver operating characteristic area under the curve of 0.85. This risk assessment was not dependent on the type of adjuvant therapy received by the patients. Even more importantly, we demonstrated that this protein signature provided an added value compared with conventional clinical parameters. Consequently, we present here a candidate serum biomarker signature able to classify patients with primary breast cancer according to their risk of developing distant recurrence, with an accuracy outperforming current procedures.
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| 5. |
- Ekstrand, Anna, et al.
(författare)
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Genetic profiles of gastroesophageal cancer: combined analysis using expression array and tiling array-comparative genomic hybridization
- 2010
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Ingår i: Cancer Genetics and Cytogenetics. - Elsevier. - 0165-4608. ; 200:2, s. 120-126
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Tidskriftsartikel (refereegranskat)abstract
- We aimed to characterize the genomic profiles of adenocarcinomas in the gastroesophageal junction in relation to cancers in the esophagus and the stomach. Profiles of gains/losses as well as gene expression profiles were obtained from 27 gastroesophageal adenocarcinomas by means of 32k high-resolution array-based comparative genomic hybridization and 27k oligo gene expression arrays, and putative target genes were validated in an extended series. Adenocarcinomas in the distal esophagus and the gastroesophageal junction showed strong similarities with the most common gains at 20q13, 8q24, 1q21-23, 5p15, 13q34, and 12q13, whereas different profiles with gains at 5p15, 7p22, 2q35, and 13q34 characterized gastric cancers. CDK6 and EGFR were identified as putative target genes in cancers of the esophagus and the gastroesophageal junction, with upregulation in one quarter of the tumors. Gains/losses and gene expression profiles show strong similarity between cancers in the distal esophagus and the gastroesophageal junction with frequent upregulation of CDK6 and EGFR, whereas gastric cancer displays distinct genetic changes. These data suggest that molecular diagnostics and targeted therapies can be applied to adenocarcinomas of the distal esophagus and gastroesophageal junction alike. (C) 2010 Elsevier Inc. All rights reserved.
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| 6. |
- Forberg, Jakob, et al.
(författare)
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An artificial neural network to safely reduce the number of ambulance ECGs transmitted for physician assessment in a system with prehospital detection of ST elevation myocardial infarction
- 2012
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Ingår i: Scandinavian Journal Of Trauma Resuscitation & Emergency Medicine. - Biomed Central Ltd. - 1757-7241. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- Background: Pre-hospital electrocardiogram (ECG) transmission to an expert for interpretation and triage reduces time to acute percutaneous coronary intervention (PCI) in patients with ST elevation Myocardial Infarction (STEMI). In order to detect all STEMI patients, the ECG should be transmitted in all cases of suspected acute cardiac ischemia. The aim of this study was to examine the ability of an artificial neural network (ANN) to safely reduce the number of ECGs transmitted by identifying patients without STEMI and patients not needing acute PCI. Methods: Five hundred and sixty ambulance ECGs transmitted to the coronary care unit (CCU) in routine care were prospectively collected. The ECG interpretation by the ANN was compared with the diagnosis (STEMI or not) and the need for an acute PCI (or not) as determined from the Swedish coronary angiography and angioplasty register. The CCU physician's real time ECG interpretation (STEMI or not) and triage decision (acute PCI or not) were registered for comparison. Results: The ANN sensitivity, specificity, positive and negative predictive values for STEMI was 95%, 68%, 18% and 99%, respectively, and for a need of acute PCI it was 97%, 68%, 17% and 100%. The area under the ANN's receiver operating characteristics curve for STEMI detection was 0.93 (95% CI 0.89-0.96) and for predicting the need of acute PCI 0.94 (95% CI 0.90-0.97). If ECGs where the ANN did not identify a STEMI or a need of acute PCI were theoretically to be withheld from transmission, the number of ECGs sent to the CCU could have been reduced by 64% without missing any case with STEMI or a need of immediate PCI. Conclusions: Our ANN had an excellent ability to predict STEMI and the need of acute PCI in ambulance ECGs, and has a potential to safely reduce the number of ECG transmitted to the CCU by almost two thirds.
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| 7. |
- 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. - 1532-8430. ; 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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| 8. |
- 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. - 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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| 9. |
- Green, Michael, et al.
(författare)
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Comparison of standard resampling methods for performance estimation of artificial neural network ensembles
- 2007
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Ingår i: Third International Conference on Computational Intelligence in Medicine and Healthcare.
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Konferensbidrag (refereegranskat)abstract
- Estimation of the generalization performance for classification within the medical applications domain is always an important task. In this study we focus on artificial neural network ensembles as the machine learning technique. We present a numerical comparison between five common resampling techniques: k-fold cross validation (CV), holdout, using three cutoffs, and bootstrap using five different data sets. The results show that CV together with holdout $0.25$ and $0.50$ are the best resampling strategies for estimating the true performance of ANN ensembles. The bootstrap, using the .632+ rule, is too optimistic, while the holdout $0.75$ underestimates the true performance.
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| 10. |
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