Predicting no-show medical appointments using machine learning

Health care centers face many issues due to the limited availability of resources, such as funds, equipment, beds, physicians, and nurses. Appointment absences lead to a waste of hospital resources as well as endangering patient health. This fact makes unattended medi- cal appointments both socially expensive and economically costly. This research aimed to build a predictive model to identify whether an appointment would be a no-show or not in order to reduce its consequences. This paper proposes a multi-stage framework to build an accurate predictor that also tackles the imbalanced property that the data exhibits. The first stage includes dimensionality reduction to compress the data into its most important components. The second stage deals with the imbalanced nature of the data. Different machine learning algorithms were used to build the classifiers in the third stage. Various evaluation metrics are also discussed and an evaluation scheme that fits the problem at hand is described. The work presented in this paper will help decision makers at health care centers to implement effective strategies to reduce the number of no-shows

Personality Judgments Based on Speaker’s Social Affective Expressions

International audienc

A Statistical Analysis of Heterogeneity on Labour Markets and Unemployment Rates in Colombia

In this paper, we study the structural factors that determine the differences in unemployment rates and in labour market performance for Colombian cities. Using cross-sectional data for 23 metropolitan areas, we apply an extension of a principal axes method—Multiple Factor Analysis for Multiple Contingency Tables (MFACT)—in order to identify unobservable factors that are relevant when disentangling the heterogeneity observed among groups of variables considered explanatory of regional unemployment differentials. Our findings suggest that differences in qualified labour supply levels, participation incentives and age structure are important when it comes to understanding regional heterogeneity in terms of labour markets and unemployment rates in Colombia. In addition, clustering methods reveal that cities that display high unemployment rates do not necessarily share the same structural characteristics; that is, labour market frictions that give rise to unemployment are not the same across Colombian cities

The Academic Dispositif: Towards a Context-Centred Discourse Analysis

This contribution outlines the dispositif approach, which combines a linguistic discourse analysis of texts with a sociological study of the social context (i.e. the dispositif understood as an institutional arrangement of practices and structures). The authors use the discourse of academic researchers to exemplify this approach. By articulating correspondence analysis of self-representations on researchers’ homepages with institutional data of sociology professors in the United Kingdom, they outline a research design that consists of three components: a linguistic analysis of texts, a sociological analysis of institutional contexts, and a theoretical account of how the two are related in the academic dispositif. The dispositif perspective on discourse aims to respond to a demand for systematic discourse research on the social and institutional contexts of discursive practices