The World Health Organization labelled the new COVID-19 breakout a public health crisis of worldwide concern on 30 January 2020, and it was named the new global pandemic in March 2020. It has had catastrophic consequences on the world economy and well-being of people and has put a tremendous strain on already-scarce healthcare systems globally, particularly in underdeveloped countries. Over 11 billion vaccine doses have already been administered worldwide, and the benefits of these vaccinations will take some time to appear. Today, the only practical approach to diagnosing COVID-19 is through the RT-PCR and RAT tests, which have sometimes been known to give unreliable results. Timely diagnosis and implementation of precautionary measures will likely improve the survival outcome and decrease the fatality rates. In this study, we propose an innovative way to predict COVID-19 with the help of alternative non-clinical methods such as supervised machine learning models to identify the patients at risk based on their characteristic parameters and underlying comorbidities. Medical records of patients from Mexico admitted between 23 January 2020 and 26 March 2022, were chosen for this purpose. Among several supervised machine learning approaches tested, the XGBoost model achieved the best results with an accuracy of 92%. It is an easy, non-invasive, inexpensive, instant and accurate way of forecasting those at risk of contracting the virus. However, it is pretty early to deduce that this method can be used as an alternative in the clinical diagnosis of coronavirus cases.

Acute-on-chronic liver failure (ACLF) is a clinical syndrome affecting patients with chronic liver disease characterized by abrupt hepatic decompensation and associated with high short-term mortality. It is characterized by intense systemic inflammation, organ failure, and a poor prognosis. Using certain liver-specific prognostic scores, and organ failures, it is possible to triage and prognosticate the outcome of patients with ACLF. This work investigates the role of the artificial neural network (ANN), which functionally mimics biological neural systems, in predicting 90-day liver disease-related mortality. This study evaluated ANN among patients with ACLF. An accuracy of 94.12% was noticed at predicting 30-day mortality and 88.2% at predicting 90-day mortality, with an area under the curve of 0.915 and 0.921, respectively. ANN plays a very important role in predicting short term mortality patients with a high accuracy. Its application in patients of ACLF is promising as it automates and eases the method of identifying those patients at a higher risk of mortality. The application of ANN in this field has a vast potential for assisting clinicians in decision making, triaging of patients requiring emergent liver transplantation, and predicting mortality and complications.