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- W4225390229 abstract "Abstract The early recognition and treatment of sepsis is essential to increase the probability of survival of the patient. Sepsis is a complex and heterogeneous syndrome influenced by the site of infection, causative microorganisms, acute organ dysfunctions and co-morbidities. So, early diagnosis is a challenge in which complex physiologic, metabolic, biochemical markers and clinical signs must be evaluated simultaneously for a reliable and early identification of sepsis. In this paper, a list of relevant variables involved in sepsis diagnosis is provided. Furthermore, to help answering the question of whether a patient is suffering from sepsis when entering the emergency room, a model based on the machine learning gradient boosting algorithm is proposed. Using three histones H2B, H3, H4 and the activated protein C together with other variables a gradient boosting classifier was trained and evaluated with cross validation. Results show that the model can achieve up to a 97% mean per-class accuracy." @default.
- W4225390229 created "2022-05-05" @default.
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- W4225390229 date "2022-05-01" @default.
- W4225390229 modified "2023-10-18" @default.
- W4225390229 title "A machine learning based predictive model for the diagnosis of sepsis" @default.
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- W4225390229 doi "https://doi.org/10.1101/2022.04.29.22274361" @default.
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