RESUMEN
With the advent of SARS-CoV-2, several studies have shown that there is a higher mortality rate in patients with diabetes and, in some cases, it is one of the side effects of overcoming the disease. However, there is no clinical decision support tool or specific treatment protocols for these patients. To tackle this issue, in this paper we present a Pharmacological Decision Support System (PDSS) providing intelligent decision support for COVID-19 diabetic patient treatment selection, based on an analysis of risk factors with data from electronic medical records using Cox regression. The goal of the system is to create real world evidence including the ability to continuously learn to improve clinical practice and outcomes of diabetic patients with COVID-19.
Asunto(s)
COVID-19 , Diabetes Mellitus , Humanos , SARS-CoV-2 , Diabetes Mellitus/terapia , Registros Electrónicos de Salud , Factores de RiesgoRESUMEN
During the COVID-19 pandemic, there was a growing need to characterise the disease. A very important aspect is the ability to measure the immunisation extent, which can be achieved using antigen microarrays that quantitively measure the presence of COVID-related antibodies. A significant limitation for these tests was the complexity of manually analysing the results, and the limited availability of software for its analysis. In this paper, we describe the development of COVID-BIOCHIP, an ad-hoc web-based solution for the automatic analysis and visualisation of COVID-19 antigen microarray data results.