RESUMEN
The waiting lists not covered by the Explicit Health Guarantee Plan for new specialty consultation in Chile increased due to the effects of the SARS-CoV-2 coronavirus (COVID-19) pandemic. This represents a problem derived from the delay in the resolution and prioritization of each case. This paper aims to describe the issue of the waiting lists in the Chilean health system and present an example of the application of Natural Language Processing (NLP). Specifically, a methodology for recognizing key information in medical narratives is described. Currently, we have a set of manually annotated medical referrals in the development of the Chilean Waiting List Corpus, with a fraction of 2,000 referrals in which the annotated medical entities were automatically normalized to the Unified Medical Language System concepts using the lexicon MedLexSp. The clinical NLP Group of the Center for Mathematical Modeling of the University of Chile, and other national NLP groups, are developing several tools and resources in medicine that can be transferred to the Chilean health system to support managing clinical text in Spanish. © 2022
RESUMEN
The waiting lists not covered by the Explicit Health Guarantee Plan for new specialty consultation in Chile increased due to the effects of the SARS-CoV-2 coronavirus (COVID-19) pandemic. This represents a problem derived from the delay in the resolution and prioritization of each case. This paper aims to describe the issue of the waiting lists in the Chilean health system and present an example of the application of Natural Language Processing (NLP). Specifically, a methodology for recognizing key information in medical narratives is described. Currently, we have a set of manually annotated medical referrals in the development of the Chilean Waiting List Corpus, with a fraction of 2,000 referrals in which the annotated medical entities were automatically normalized to the Unified Medical Language System concepts using the lexicon MedLexSp. The clinical NLP Group of the Center for Mathematical Modeling of the University of Chile, and other national NLP groups, are developing several tools and resources in medicine that can be transferred to the Chilean health system to support managing clinical text in Spanish.
RESUMEN
Nowadays there is a world pandemic of a challenging respiratory illness, COVID-19. A large part of COVID-19 patients evolves to severe or fatal complications and require an ICU admission. COVID-19 mortality rate approaches 30% due to complications such as obstruction of the trachea and bronchi of patients during the ICU stay. An endotracheal obstruction occurring during any moment in a COVID-19 patient ICU stay is one of the most complicated situations that clinicians must face and solve. Therefore, it is very important to know in advance when a COVID-19 patient could enter in the pre-obstruction zone. In this work we present an intelligent computing solution to predict endotracheal obstruction for COVID-19 patients in ICU. It is called the Binomial Gate LSTM (BigLSTM), a new and innovative deep modular neural architecture based on the recurrent neural network LSTM. Its main feature is its ability to handle missing data and to deal with time series with no regular sample frequency. These are the main characteristics of the BigLSTM information environment. This ability is implemented in BigLSTM by an information redundancy injection mechanism and how it copes with time control. We applied BigLSTM with first wave COVID-19 patients in ICU of Complejo Hospitalario Universitario Insular Materno Infantil. Encouraging results, even while working with a very small data set, indicate that our developed computing solution is going forwards towards an efficient intelligent prediction system which is very appropriate for this kind of problem. © 2021, Springer Nature Switzerland AG.
RESUMEN
Background: ACE2 is a component of the renin-angiotensin system(RAS) that mainly degrades angiotensin II to angiotensin(1-7). It is expressed in renal tubular cells. Lung type 2 alveolar cells also express ACE2 where it acts as a receptor for SARS-CoV-2, which is responsible for the current coronavirus disease 2019(COVID-19) pandemic. A controversy raised regarding the use of RAS blockers in COVID-19 patients despite its demonstrated efficacy in cardiovascular disease. We studied the effect of ramipril on ACE2 expression in experimental diabetes. Methods: 12 weeks old diabetic db/db mice were given ramipril(8 mg/Kg/day) or vehicle during 8 weeks. db/m mice were used as controls. ACE2 expression and enzymatic activity were studied in kidney, heart and lung. Results: In non-treated db/db, ACE2 mRNA expression was increased in kidney(p<0.0001) and ramipril treatment reversed this effect. In heart, ACE2 expression decreased in db/db when compared to db/m(p=0.028) and ramipril had no effect. We found no differences in lung. ACE2 enzymatic activity was increased 23% in kidney and 22% in lung of db/db mice when compared to db/m. Ramipril treatment decreased ACE2 activity 25% in the lung and 13% in the kidney when compared to untreated db/db. In the heart, ACE2 activity tended to decrease in db/db mice when compared to db/m, and increased with ramipril, but did not exceed the cardiac ACE2 activity of the db/m. Conclusions: ACE2 is increased in the kidney and in the lung, and decreased in the heart of diabetic mice. Ramipril treatment restores ACE2. Our results suggest that diabetes and hypertension may per se be risk factors for COVID-19 and not the treatment with ACE inhibitors, which may exert a protective effect on COVID-19 infection.