| 1. |
|
|
| 2. |
|
|
| 3. |
- Van Holsbeke, Caroline, et al.
(författare)
-
Ultrasound Experience Substantially Impacts on Diagnostic Performance and Confidence when Adnexal Masses Are Classified Using Pattern Recognition
- 2010
-
Ingår i: Gynecologic and Obstetric Investigation. - : S. Karger AG. - 1423-002X .- 0378-7346. ; 69:3, s. 160-168
-
Tidskriftsartikel (refereegranskat)abstract
- Aim: To determine how accurately and confidently examiners with different levels of ultrasound experience can classify adnexal masses as benign or malignant and suggest a specific histological diagnosis when evaluating ultrasound images using pattern recognition. Methods: Ultrasound images of selected adnexal masses were evaluated by 3 expert sonologists, 2 senior and 4 junior trainees. They were instructed to classify the masses using pattern recognition as benign or malignant, to state the level of confidence with which this classification was made and to suggest a specific histological diagnosis. Sensitivity, specificity, accuracy and positive and negative likelihood ratios (LR+ and LR-) with regard to malignancy were calculated. The area under the receiver operating characteristic curve (AUC) of pattern recognition was calculated by using six levels of diagnostic confidence. Results: 166 masses were examined, of which 42% were malignant. Sensitivity with regard to malignancy ranged from 80 to 86% for the experts, was 70 and 84% for the 2 senior trainees and ranged from 70 to 86% for the junior trainees. The specificity of the experts ranged from 79 to 91%, was 77 and 89% for the senior trainees and ranged from 59 to 83% for the junior trainees. The experts were uncertain about their diagnosis in 4-13% of the cases, the senior trainees in 15-20% and the junior trainees in 67-100% of the cases. The AUCs ranged from 0.861 to 0.922 for the experts, were 0.842 and 0.855 for the senior trainees, and ranged from 0.726 to 0.795 for the junior trainees. The experts suggested a correct specific histological diagnosis in 69-77% of the cases. All 6 trainees did so significantly less often (22-42% of the cases). Conclusion: Expert sonologists can accurately classify adnexal masses as benign or malignant and can successfully predict the specific histological diagnosis in many cases. Whilst less experienced operators perform reasonably well when predicting the benign or malignant nature of the mass, they do so with a very low level of diagnostic confidence and are unable to state the likely histology of a mass in most cases. Copyright (C) 2009 S. Karger AG, Basel
|
|
| 4. |
- Falck, Tillmann, et al.
(författare)
-
Least-Squares Support Vector Machines for the identification of Wiener-Hammerstein systems
- 2012
-
Ingår i: Control Engineering Practice. - : Elsevier BV. - 0967-0661 .- 1873-6939. ; 20:11, s. 1165-1174
-
Tidskriftsartikel (refereegranskat)abstract
- This paper considers the identification of Wiener-Hammerstein systems using Least-Squares Support Vector Machines based models. The power of fully black-box NARX-type models is evaluated and compared with models incorporating information about the structure of the systems. For the NARX models it is shown how to extend the kernel-based estimator to large data sets. For the structured model the emphasis is on preserving the convexity of the estimation problem through a suitable relaxation of the original problem. To develop an empirical understanding of the implications of the different model design choices, all considered models are compared on an artificial system under a number of different experimental conditions. The obtained results are then validated on the Wiener-Hammerstein benchmark data set and the final models are presented. It is illustrated that black-box models are a suitable technique for the identification of Wiener-Hammerstein systems. The incorporation of structural information results in significant improvements in modeling performance.
|
|
| 5. |
|
|
| 6. |
- Barrett, Jennifer H., et al.
(författare)
-
Genome-wide association study identifies three new melanoma susceptibility loci
- 2011
-
Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 43:11, s. 1108-1113
-
Tidskriftsartikel (refereegranskat)abstract
- We report a genome-wide association study for melanoma that was conducted by the GenoMEL Consortium. Our discovery phase included 2,981 individuals with melanoma and 1,982 study-specific control individuals of European ancestry, as well as an additional 6,426 control subjects from French or British populations, all of whom were genotyped for 317,000 or 610,000 single-nucleotide polymorphisms (SNPs). Our analysis replicated previously known melanoma susceptibility loci. Seven new regions with at least one SNP with P < 10(-5) and further local imputed or genotyped support were selected for replication using two other genome-wide studies (from Australia and Texas, USA). Additional replication came from case-control series from the UK and The Netherlands. Variants at three of the seven loci replicated at P < 10(-3): an SNP in ATM (rs1801516, overall P = 3.4 x 10(-9)), an SNP in MX2 (rs45430, P = 2.9 x 10-9) and an SNP adjacent to CASP8 (rs13016963, P = 8.6 x 10(-10)). A fourth locus near CCND1 remains of potential interest, showing suggestive but inconclusive evidence of replication (rs1485993, overall P = 4.6 x 10(-7) under a fixed-effects model and P = 1.2 x 10(-3) under a random-effects model). These newly associated variants showed no association with nevus or pigmentation phenotypes in a large British case-control series.
|
|
| 7. |
- Daemen, Anneleen, et al.
(författare)
-
Improved modeling of clinical data with kernel methods
- 2012
-
Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 1873-2860 .- 0933-3657. ; 54:2, s. 103-114
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. Methods: When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. Results: The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. Conclusion: For clinical data consisting of variables of different types, the proposed kernel function which takes into account the type and range of each variable - has shown to be a better alternative for linear and non-linear classification problems. (C) 2011 Elsevier B.V. All rights reserved.
|
|
| 8. |
- Falck, Tillmann, et al.
(författare)
-
Segmentation of Time Series from Nonlinear Dynamical Systems
- 2011
-
Ingår i: Proceedings of the 18th IFAC World Congress. - 9783902661937 ; , s. 13209-13214
-
Konferensbidrag (refereegranskat)abstract
- Segmentation of time series data is of interest in many applications, as for example in change detection and fault detection. In the area of convex optimization, the sum-of-norms regularization has recently proven useful for segmentation. Proposed formulations handle linear models, like ARX models, but cannot handle nonlinear models. To handle nonlinear dynamics, we propose integrating the sum-of-norms regularization with a least squares support vector machine (LS-SVM) core model. The proposed formulation takes the form of a convex optimization problem with the regularization constant trading off the fit and the number of segments.
|
|
| 9. |
|
|
| 10. |
- Ljung, Lennart, 1946-, et al.
(författare)
-
Comparison of Three Classes of Identification Methods
- 1994
-
Ingår i: Proceedings of the 10th IFAC Symposium on System Identification. - Linköping : Linköping University. - 9780080422251 ; , s. 175-180
-
Rapport (övrigt vetenskapligt/konstnärligt)
|
|
