IEEE Trans Neural Netw Learn Syst
April 2024
Computer vision syndrome causes vision problems and discomfort mainly due to dry eye. Several studies show that dry eye in computer users is caused by a reduction in the blink rate and an increase in the prevalence of incomplete blinks. In this context, this article introduces Eye-LRCN, a new eye blink detection method that also evaluates the completeness of the blink.
View Article and Find Full Text PDFIntroduction: Chronic Lymphocytic Leukemia (CLL) is a disease with highly heterogeneous clinical course. A key goal is the prediction of patients with high risk of disease progression, which could benefit from an earlier or more intense treatment. In this work we introduce a simple methodology based on machine learning methods to help physicians in their decision making in different problems related to CLL.
View Article and Find Full Text PDFIEEE/ACM Trans Comput Biol Bioinform
April 2014
Genome-wide association studies (GWA) try to identify the genetic polymorphisms associated with variation in phenotypes. However, the most significant genetic variants may have a small predictive power to forecast the future development of common diseases. We study the prediction of the risk of developing a disease given genome-wide genotypic data using classifiers with a reject option, which only make a prediction when they are sufficiently certain, but in doubtful situations may reject making a classification.
View Article and Find Full Text PDFThe functional characterization of genes involved in many complex traits (phenotypes) of plants, animals, or humans can be studied from a computational point of view using different tools. We propose prediction--from the machine learning point of view--to search for the genetic basis of these traits. However, trying to predict an exact value of a phenotype can be too difficult to obtain a confident model, but predicting an approximation, in the form of an interval of values, can be easier.
View Article and Find Full Text PDFObjective: Survival probability predictions in critically ill patients are mainly used to measure the efficacy of intensive care unit (ICU) treatment. The available models are functions induced from data on thousands of patients. Eventually, some of the variables used for these purposes are not part of the clinical routine, and may not be registered in some patients.
View Article and Find Full Text PDF