Machine Learning Based Automated Detection of Sepsis: A Study

Swarup Nandi; Ranadhir Debnath; Swanirbhar Majumder

Abstract

Sepsis is a serious condition that occurs when the body's response to infection leads to tissue damage, organ failure, or death. Human body generally releases chemicals into the blood vessels to fight any kind of infection. Approximately 30 million people suffer from sepsis and 6 million people die from sepsis every year according to WHO. Around 4 million 200 thousand new-born and children are affected by sepsis all over the world. Early prediction of sepsis and treatment are necessary for improving sepsis results where every hour of deferred treatment has been related with approximately a 4–8% expansion in mortality. In response to the PhysioNet/CinC Challenge 2019, many researchers developed several early detection techniques for sepsis. In this work, the various early detection techniques are discussed and how they work is explained. We have studied and compared techniques developed using Machine Learning in response to PhysioNet/CinC Challenge 2019 for early prediction of sepsis using clinical data. Apart from that we have presented three different methods to detect sepsis early, using the data taken from PhysioNet/CinC Challenge 2019 based on Random Forest, Decision Tree and K Nearest Neighbors.

Abstract

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