Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach.

Background: In December 2020, the COVID-19 disease was confirmed in 1,665,775 patients and caused 45,784 deaths in Spain. At that time, health decision support systems were identified as crucial against the pandemic.

Methods: This study applies Deep Learning techniques for mortality prediction of COVID-19 patients.

PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts.

Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text.