Development of a Web Application based on Machine Learning for screening esophageal varices in cirrhosis.

INTRODUCTION: Esophageal varices (EV) are a common manifestation of portal hypertension in cirrhotic patients. Upper gastrointestinal endoscopy (UGE) is the gold standard for diagnosing EV. However, it is an invasive examination with a relatively high cost. AIM: To develop a machine learning model for the prediction of EV in cirrhotic patients. METHODS: This is a cross-sectional observational study including all cirrhotic patients, for whom an UGE was performed, between January 2010 and December 2019. We adopted a structured methodical approach with reference to CRISP-DM (Cross-Industry Standard Process for Data Mining). The different steps carried out were: data collection and preparation, modelization, and deployment of the predictive models in a web application. RESULTS: We included 166 patients, 92 women (55.4%) and 74 men (44.6%). The mean age was 57.2 years. In UGE, 16 patients (9.6%) did not have EV. Other patients had EV grade 1 in 41 cases (24.7%), grade 2 in 81 cases (24.7%) and grade 3 in 28 cases (16.9%). After the selection phase, among the 36 initial variables, 19 were retained. Three machine learning models have been developed with a performance of 90%. CONCLUSIONS: We developed a machine learning model combining several clinical and para-clinical variables for the prediction of EV in cirrhotic patients.

The extent of forest in dryland biomes.

Dryland biomes cover two-fifths of Earth's land surface, but their forest area is poorly known. Here, we report an estimate of global forest extent in dryland biomes, based on analyzing more than 210,000 0.5-hectare sample plots through a photo-interpretation approach using large databases of satellite imagery at (i) very high spatial resolution and (ii) very high temporal resolution, which are available through the Google Earth platform. We show that in 2015, 1327 million hectares of drylands had more than 10% tree-cover, and 1079 million hectares comprised forest. Our estimate is 40 to 47% higher than previous estimates, corresponding to 467 million hectares of forest that have never been reported before. This increases current estimates of global forest cover by at least 9%.