Biomedical subjects

Torsten Hothorn

Publications and source records attributed to Torsten Hothorn.

4 recordsLinked to original sources

PubMed2004

Bagging survival trees.

Predicted survival probability functions of censored event free survival are improved by bagging survival trees. We suggest a new method to aggregate survival trees in order to obtain better predictions for breast cancer and lymphoma patients. A set of survival trees based on B bootstrap samples is computed. We define the aggregated Kaplan-Meier curve of a new observation by the Kaplan-Meier curve of all observations identified by the B leaves containing the new observation. The integrated Brier score is used for the evaluation of predictive models. We analyse data of a large trial on node positive breast cancer patients conducted by the German Breast Cancer Study Group and a smaller 'pilot' study on diffuse large B-cell lymphoma, where prognostic factors are derived from microarray expression values. In addition, simulation experiments underline the predictive power of our proposal.

Breast Neoplasms↗

PubMed2003

Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy.

Diagnosis based on medical image data is common in medical decision making and clinical routine. We discuss a strategy to derive a classifier with good performance on clinical image data and to justify the properties of the classifier by an adapted simulation model of image data. We focus on the problem of classifying eyes as normal or glaucomatous based on 62 routine explanatory variables derived from laser scanning images of the optic nerve head. As learning sample we use a case-control study of 98 normal and 98 glaucomatous subjects matched by age and sex. Aggregating multiple unstable classifiers allows substantial reduction of misclassification error in many applications and bench mark problems. We investigate the performance of various classifiers for the clinical learning sample as well as for a simulation model of eye morphologies. Bagged classification trees (bagged-CTREE) are compared to single classification trees and linear discriminant analysis (LDA). We additionally compare three estimators of misclassification error: 10-fold cross-validation, the 0.632+ bootstrap and the out-of-bag estimate. In summary, the application of our strategy of a knowledge-based decision support shows that bagged classification trees perform best for glaucoma classification.

Case-Control Studies↗

PubMed2003

New glaucoma classification method based on standard Heidelberg Retina Tomograph parameters by bagging classification trees.

PURPOSE: In this article we propose and evaluate nonparametric tree classifiers that can handle non-normal data and a large number of possible predictors using the full set of standard Heidelberg Retina Tomograph measurements for classifying glaucoma. METHODS: The classifiers were trained and tested using standard Heidelberg Retina Tomograph parameters from examinations of 98 subjects with glaucoma and 98 normal subjects of the Erlangen Glaucoma Registry. All patients and control subjects were evaluated by 15 degrees -optic disc stereographs, Heidelberg Retina Tomograph measurements, standard computerized white-in-white perimetry, and 24-hour-intraocular pressure profiles. The subjects were matched by age and sex. Standard classification trees as well as bagged classification trees were used. The classification outcome of the trees was compared with the classification by two published linear discriminant functions based on Heidelberg Retina Tomograph variables with respect to their cross-validated misclassification error. RESULTS: The bagged classification tree had the lowest misclassification error estimate of 14.8% with a sensitivity of 81.6% at a specificity of 88.8%. The cross-validated error rates of the two linear discriminant function procedures were 20.4% (sensitivity 82.6%, specificity 76.7%) and 20.6% (sensitivity 81.4%, specificity 77.3%) for our set of observations. Bagged classification trees were able to reduce the misclassification error of glaucoma classification. CONCLUSIONS: Bagged classification trees promise to be a new and efficient approach for glaucoma classification using morphometric 2- and 3-dimensional data derived from the Heidelberg Retina Tomograph, taking into account all given variables.

Case-Control Studies↗

PubMed2002

Longitudinal concentrations of vitamin B(12) and vitamin B(12)-binding proteins during uncomplicated pregnancy.

BACKGROUND: Because reference values for vitamin B(12) concentrations and vitamin B(12)-binding capacities for pregnant women have not been established, the reference values for nonpregnant women are often applied to assess vitamin B(12) status. The aim of the present study was to describe ranges of biochemical indices of vitamin B(12) status, including red blood cell (RBC) vitamin B(12), saturated and unsaturated cobalamin-binding proteins, and binding capacities in all trimesters of uncomplicated pregnancy. METHODS: A total of 39 healthy pregnant women with long-term daily intake of vitamin B(12) >2.6 microg/day and uncomplicated pregnancies participated in the study throughout their pregnancies. RBCs and serum vitamin B(12), holo-haptocorrin, unsaturated cobalamin-binding proteins, unsaturated and total vitamin B(12)-binding capacities, total homocysteine (tHcy), and RBC count were assessed in weeks 9-12, 20-22, and 36-38 of gestation. RESULTS: Significant changes in vitamin B(12) status occurred in the course of pregnancy. Serum vitamin B(12) concentrations and percentage of saturation of vitamin B(12)-binding proteins decreased steadily throughout pregnancy. In the third trimester, 35% of the participants had serum vitamin B(12) concentrations <150 pmol/L and 68.6% had <15% saturation of total vitamin B(12)-binding capacities, but no women had RBC vitamin B(12) concentrations <148 pmol/L. However, the decrease in these indices was not associated with reduced hemoglobin concentrations or RBC count or with increased tHcy concentrations. CONCLUSIONS: Our findings suggest that the reference values for vitamin B(12) status in nonpregnant women may not be applicable to pregnant women.

Diet↗