In “ Literature review” section, we examine some of the most relevant literature present in machine learning with a healthcare perspective. This preference for blood tests is not coincidental: most of them are well-standardized and usually inexpensive to perform, accessible in most situations, even for developing countries. These data comprise several different laboratory tests performed on patients (mostly blood tests). We used data available in, which joins laboratory test data from the Sírio Libanês Hospital, Albert Einstein Israeli Hospital, and Fleury Laboratories (all located in the city of São Paulo, Brazil). Our aim is to create the basis of a decision system that can be used by anyone interested in replicating and estimating such outcomes, with the capability to expand the proposed method to deal with other diseases when needed. We propose an analytical approach that leverages the most recent discoveries in each one of these areas and uses laboratory blood test data to estimate the probability of one given patient to require special-care treatment, also estimating the number of days the same patient will be under such care. Our main motivation is to unify subjects, such as Machine Learning, Optimization, Hospital Planning and applied AI to serve the purpose of using hospital resources responsibly and improve the quality of care provided to patients. Īs pointed out by, the massive amount of data acquired from several sources should be put into fair use for intensive training of machine learning algorithms to better understand the disease, the patients, and possible prognosis, enabling informed decision-making. The disease is spreading quickly, and social distancing measures are being phased out in several countries despite recommendations on the contrary issued by the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC). There are various independent reports that indicate a high occupancy rate in intensive care units with facilities to support patients who have severe respiratory tract failure and related conditions, thus creating a unique opportunity to solve this problem with scientific rigor helping to improve this difficult situation. The disease is putting tremendous pressure on health care services and there is no strong consensus on what measures are the most effective in terms of dealing with it. The COVID-19 pandemic is a considerable challenge for Brazil and many other countries around the world.
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