Biomedical subjects

Jaume March

Publications and source records attributed to Jaume March.

4 recordsLinked to original sources

PubMed2005

[Short Zarit scale in palliative care].

BACKGROUND AND OBJECTIVE: We aimed to reduce the Zarit scale (ZS) for the assessment of caregiver burden in palliative care. PATIENTS AND METHOD: ZS was performed in 61 caregivers of palliative care patients admitted to hospital. The field explored by each item was decided by an expert committee and a logistic regression and ROC curve were performed. RESULTS: ZS reduction to 7 items was achieved with a sensitivity and specificity of 100%. CONCLUSIONS: A reduced Zarit scale is useful to identify caregiver burden in palliative care. It is also easy to use.

Caregivers↗

PubMed2004

[Methodological approach to the use of artificial neural networks for predicting results in medicine].

In clinical practice, there is an increasing interest in obtaining adequate models of prediction. Within the possible available alternatives, the artificial neural networks (ANN) are progressively more used. In this review we first introduce the ANN methodology, describing the most common type of ANN, the Multilayer Perceptron trained with backpropagation algorithm (MLP). Then we compare the MLP with the Logistic Regression (LR). Finally, we show a practical scheme to make an application based on ANN by means of an example with actual data. The main advantage of the RN is its capacity to incorporate nonlinear effects and interactions between the variables of the model without need to include them a priori. As greater disadvantages, they show a difficult interpretation of their parameters and large empiricism in their process of construction and training. ANN are useful for the computation of probabilities of a given outcome based on a set of predicting variables. Furthermore, in some cases, they obtain better results than LR. Both methodologies, ANN and LR, are complementary and they help us to obtain more valid models.

Artificial Intelligence↗

PubMed2003

Estimating conditional distributions using a method based on S-distributions reference percentile curves for body mass index in Spanish children.

BACKGROUND: Reference intervals are a fundamental tool for characterizing the health status in a given population and play a central role in defining diagnostic values in clinical applications. Estimation of the conditional distribution of a variable, as the body mass index (BMI), is necessary for providing reference values when there is a trend as a function of the covariate. SUBJECTS AND METHOD: We studied 1453 boys and young between 5 and 16 years old measured in a study carried out in the schools of Lleida (Spain). BMI conditional distributions with age have been derived using a new parametric method based on the one proposed by Sorribas et al. [Stat. Med. (2000) 19:697-713]. This method is based on S-distributions as a parametric model for the distribution and uses maximum likelihood estimation of the conditional distribution. RESULTS: The methods commonly used for estimating reference curves are based on a smoothing of sample quantiles using different techniques. However, these methods do not provide information on the conditional distribution of the target variable. Our method provides an estimation of such distribution and the corresponding reference curves for the quantiles as a function of a covariate, in our case age. CONCLUSIONS: The suggested methodology provides appropriate reference quantiles for the BMI. Our results allow characterizing the change in distribution within the age range considered. Besides describing a raise in BMI with age, we observe an increase in dispersion around puberty. This must be considered when using BMI as a diagnostic variable.

Adolescent↗

PubMed2002

A new parametric method based on S-distributions for computing receiver operating characteristic curves for continuous diagnostic tests.

Receiver operating characteristic (ROC) curves provides a method for evaluating the performance of a diagnostic test. These curves represent the true positive ratio, that is, the true positives among those affected by the disease, as a function of the false positive ratio, that is, the false positives among the healthy, corresponding to each possible value of the diagnostic variable. When the diagnostic variable is continuous, the corresponding ROC curve is also continuous. However, estimation of such curve through the analysis of sample data yields a step-line, unless some assumption is made on the underlying distribution of the considered variable. Since the actual distribution of the diagnostic test is seldom known, it is difficult to select an appropriate distribution for practical use. Data transformation may offer a solution but also may introduce a distortion on the evaluation of the diagnostic test. In this paper we show that the distribution family known as the S-distribution can be used to solve this problem. The S-distribution is defined as a differential equation in which the dependent variable is the cumulative. This special form provides a highly flexible family of distributions that can be used as models for unknown distributions. It has been shown that classical statistical distributions can be represented accurately as S-distributions and that they occur in a definite subspace of the parameter space corresponding to the whole S-distribution family. Consequently, many other distributional forms that do not correspond to known distributions are provided by the S-distribution. This property can be used to model observed data for unknown distributions and is very useful in constructing parametric ROC curves in those cases. After fitting an S-distribution to the observed samples of diseased and healthy populations, ROC curve computation is straightforward. A ROC curve can be considered as the solution of a differential equation in which the dependent variable is the ratio of true positives and the independent variable is the ratio of false positives. This equation can be easily obtained from the S-distributions fitted to observed data. Using these results, we can compute pointwise confidence bands for the ROC curve and the corresponding area under the curve. We shall compare this approach with the empirical and the binormal methods for estimating a ROC curve to show that the S-distribution based method is a useful parametric procedure.

Area Under Curve↗