Biomedical subjects

B De Moor

Publications and source records attributed to B De Moor.

22 records · Page 2Linked to original sources

PubMed1999

Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses.

OBJECTIVE: The aim of this study was to generate and evaluate artificial neural network (ANN) models from simple clinical and ultrasound-derived criteria to predict whether or not an adnexal mass will have histological evidence of malignancy. DESIGN: The data were collected prospectively from 173 consecutive patients who were scheduled to undergo surgical investigations at the University Hospitals, Leuven, between August 1994 and August 1996. The outcome measure was the histological classification of excised tissues as malignant (including borderline) or benign. METHODS: Age, menopausal status and serum CA 125 levels and sonographic features of the adnexal mass were encoded as variables. The ANNs were trained on a randomly selected set of 116 patient records and tested on the remainder (n = 57). The performance of each model was evaluated using receiver operating characteristic (ROC) curves and compared with corresponding data from an established risk of malignancy index (RMI) and a logistic regression model. RESULTS: There were 124 benign masses, five of borderline malignancy and 44 invasive cancers (of which 29% were metastatic); 37% of patients with a malignant or borderline tumor had stage I disease. The best ANN gave an area under the ROC curve of 0.979 for the whole dataset, a sensitivity of 95.9% and specificity of 93.5%. The corresponding values for the RMI were 0.882, 67.3% and 91.1%, and for the logistic regression model 0.956, 95.9% and 85.5%, respectively. CONCLUSION: An ANN can be trained to provide clinically accurate information, on whether or not an adnexal mass is malignant, from the patient's menopausal status, serum CA 125 levels, and some simple ultrasonographic criteria.

Adnexal Diseases↗

PubMed1994

Subclavian vein hemodialysis catheters: advantages and disadvantages.

The pros and cons of subclavian vein hemodialysis catheters are reviewed. The subclavian vein catheter offers rapid and adequate vascular access. Other advantages are preservation of patient mobility and the ease with which the dressing can be secured. In contrast, subclavian vein stenosis has recently been identified as an important long-term complication of subclavian vein catheters that seriously compromises the creation and long-term viability of later ipsilateral arteriovenous fistulas. For this reason, we recommend restricting use of the subclavian hemodialysis catheter for acute situations and preferential use of internal jugular catheters for more chronic approaches because they better preserve venous integrity.

Catheterization, Central Venous↗

PubMed1990

Comparison of SVD methods to extract the foetal electrocardiogram from cutaneous electrode signals.

The paper presents and compares three methods making use of the singular value decomposition (SVD) of a matrix to extract the foetal electrocardiogram (FECG) from cutaneously recorded electrode signals. The first method constructs a set of orthogonal foetal signals (the so-called principal foetal signals) from the recordings, but needs electrode positions far from the foetal heart, in addition to the abdominal electrodes that pick up a mixture of maternal and foetal electrocardiogram. An online adaptive algorithm has been developed such that a real-time implementation becomes feasible. The second method is a new online approach to a technique presented by van Oosterom. Although this method has some important drawbacks and is suboptimal as far as foetal signal-to-noise ratio is concerned, it is still very useful when only a foetal trigger is required, as the signal obtained is not a complete FECG. Finally, a third method is proposed, based on the generalised SVD and interpreted with the new concept of oriented signal-to-signal ratio. An online version is also presented for this method and some results are shown.

Electrocardiography↗

PubMedDate not supplied

Handling missing values in support vector machine classifiers.

This paper discusses the task of learning a classifier from observed data containing missing values amongst the inputs which are missing completely at random. A non-parametric perspective is adopted by defining a modified risk taking into account the uncertainty of the predicted outputs when missing values are involved. It is shown that this approach generalizes the approach of mean imputation in the linear case and the resulting kernel machine reduces to the standard Support Vector Machine (SVM) when no input values are missing. Furthermore, the method is extended to the multivariate case of fitting additive models using componentwise kernel machines, and an efficient implementation is based on the Least Squares Support Vector Machine (LS-SVM) classifier formulation.

Algorithms↗