Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach.

Background: Accurately predicting which patients with chronic heart failure (CHF) are particularly vulnerable for adverse outcomes is of crucial importance to support clinical decision making. The goal of the current study was to examine the predictive value on long term heart failure (HF) hospitalisation and all-cause mortality in CHF patients, by exploring and exploiting machine learning (ML) and traditional statistical techniques on a Dutch health insurance claims database.

Methods: Our study population consisted of 25,776 patients with a CHF diagnosis code between 2012 and 2014 and one year and three years follow-up HF hospitalisation (1446 and 3220 patients respectively) and all-cause mortality (2434 and 7882 patients respectively) were measured from 2015 to 2018.

Finding Dutch natives in online forums.

Law enforcement agencies have a restricted area in which their powers apply, which is called their jurisdiction. These restrictions also apply to the Internet. However, on the Internet, the physical borders of the jurisdiction, typically country borders, are hard to discover.

Visual word ambiguity.

This paper studies automatic image classification by modeling soft assignment in the popular codebook model. The codebook model describes an image as a bag of discrete visual words selected from a vocabulary, where the frequency distributions of visual words in an image allow classification. One inherent component of the codebook model is the assignment of discrete visual words to continuous image features.

Source camera identification for heavily JPEG compressed low resolution still images.

In this research, we examined whether fixed pattern noise or more specifically Photo Response Non-Uniformity (PRNU) can be used to identify the source camera of heavily JPEG compressed digital photographs of resolution 640 x 480 pixels. We extracted PRNU patterns from both reference and questioned images using a two-dimensional Gaussian filter and compared these patterns by calculating the correlation coefficient between them. Both the closed and open-set problems were addressed, leading the problems in the closed set to high accuracies for 83% for single images and 100% for around 20 simultaneously identified questioned images.

A cellular coevolutionary algorithm for image segmentation.

Clustering is inherently a difficult problem, both with respect to the definition of adequate models as well as to the optimization of the models. We present a model for the cluster problem that does not need knowledge about the number of clusters a priori. This property is among others useful in the image segmentation domain, which we especially address.

LESS: a model-based classifier for sparse subspaces.

In this paper, we specifically focus on high-dimensional data sets for which the number of dimensions is an order of magnitude higher than the number of objects. From a classifier design standpoint, such small sample size problems have some interesting challenges. The first challenge is to find, from all hyperplanes that separate the classes, a separating hyperplane which generalizes well for future data.

The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier.

We present the Nearest Subclass Classifier (NSC), which is a classification algorithm that unifies the flexibility of the nearest neighbor classifier with the robustness of the nearest mean classifier. The algorithm is based on the Maximum Variance Cluster algorithm and, as such, it belongs to the class of prototype-based classifiers. The variance constraint parameter of the cluster algorithm serves to regularize the classifier, that is, to prevent overfitting.

A protocol for building and evaluating predictors of disease state based on microarray data.

Motivation: Microarray gene expression data are increasingly employed to identify sets of marker genes that accurately predict disease development and outcome in cancer. Many computational approaches have been proposed to construct such predictors. However, there is, as yet, no objective way to evaluate whether a new approach truly improves on the current state of the art.