2022
DOI: 10.1186/s40001-022-00843-4
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Predicting risk of sepsis, comparison between machine learning methods: a case study of a Virginia hospital

Abstract: Sepsis is an inflammation caused by the body's systemic response to an infection. The infection could be a result of many diseases, such as pneumonia, urinary tract infection, and other illnesses. Some of its symptoms are fever, tachycardia, tachypnea, etc. Unfortunately, sepsis remains a critical problem at the hospitals and leads to many issues, such as increasing mortality rate, health care costs, and health care utilization. Early detection of sepsis in patients can help respond quickly, take preventive … Show more

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Cited by 3 publications
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“…ML algorithm utilized for mortality prediction facilitates impartial processing of extensive clinical variables, allowing for the identi cation of crucial factors in a non-supervised manner. These algorithms empower the identi cation of distinct patient phenotypes and enable the visualization of the quantitative contribution of each variable to the outcome [12].…”
Section: Introductionmentioning
confidence: 99%
“…ML algorithm utilized for mortality prediction facilitates impartial processing of extensive clinical variables, allowing for the identi cation of crucial factors in a non-supervised manner. These algorithms empower the identi cation of distinct patient phenotypes and enable the visualization of the quantitative contribution of each variable to the outcome [12].…”
Section: Introductionmentioning
confidence: 99%