COVID-19 patient management guidelines
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Unsupervised machine learning approaches are recognized as tools for constructing electronic health record-based risk models to select important predictors automatically from numerous features and face random or systematic errors, following the generalization of evidence-based approaches that are important to produce early prevention guidelines. With the use of machine learning in medical databases collecting data or early-stage information, dealing with new or unknown respiratory infections caused by viruses as recently for Covid-19, the disease could be more accurately figured and predicted towards better management of the fast-growing patient population. Convolutional neural networks that have been commonly used on image data could be a useful tool to improve the differential diagnosis and proper manipulation of respiratory infections. In this project, we introduce a fast-responding approach for guideline generation during the early stages of infection pandemics with the induction of statistical models and A.I. algorithms translating the output of unsupervised learning of decision support Natural Language Processing applications.

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The final output will be an online adaptive learning system that will generate predictive analytic models helping healthcare professionals to target high-risk populations and optimize prevention strategies using at the same time properly, all available health care resources.

 

 

 

 

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