Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data.

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/ or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data. As shown by our results, the CAD proposal is able to detect PD with [Formula: see text] of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.

A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model.

A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of an indicator matrix; that is, in terms of the inverse problem of regressing the observations. Both approaches, i.e. GLM and LRM, apply to different domains, the observation and the label domains, and are linked by a normalization value in the least-squares solution. Subsequently, we derive a more refined predictive statistical test: the linear Support Vector Machine (SVM), that maximizes the class margin of separation within a permutation analysis. This MLE-based inference employs a residual score and associated upper bound to compute a better estimation of the actual (real) error. Experimental results demonstrate how parameter estimations derived from each model result in different classification performance in the equivalent inverse problem. Moreover, using real data, the MLE-based inference including model-free estimators demonstrates an efficient trade-off between type I errors and statistical power.

[Detection of epidemics from the Compulsory Disease Report. The case of measles in Las Palmas]. / Detección de epidemias a partir de las Enfermedades de Declaración Obligatoria. El caso del sarampión en Las Palmas.

The system of Compulsory Disease Reports (CDR) is an epidemiological monitoring regarding cases of contagious diseases which has been in use in Spain for over forty years. In 1982, new additions were made to the list of diseases for which a report is to be filed and, since that time, it was insisted upon that the centers falling under the National Health Institute be included among those having to file said reports. Nevertheless, many physicians question the need for this system and its usefulness. The cases of measles for which reports were filed in the province of Las Palmas from 1983 to 1988 were studied. Two waves of epidemics, occurring in early 1983 and in mid-1988, were observed, being patently distinguished from the noise of the reports between. Both waves of epidemics coincide with the hospitalization of cases of measles-related pneumonia in the pediatric hospital of that province. Based on these waves of epidemics, the importance of carrying out accurate measles vaccination studies for immunizing the populations against this disease is implied. The epidemiological importance of this CDR system is discussed both from a historic point of view and from that of epidemiological monitoring, and the need of drawing up and using operational definitions for each one of the diseases which is to be reported is reiterated. In conclusion, a positive evaluation of the usefulness of the CDR system, in spite of its current limitations, is provided, for which reason the importance of continuing to improve this system is stressed.