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Title: | Outcome Prediction in Infectious Disease |
Authors: | Chen Lie, Khie Yonggara, Yosia Pasaribu, Adeline Shakinah, Sharifah Nainggolan, Leonard |
Keywords: | Outcome prediction, Infectious Disease, Sepsis |
Issue Date: | 2024 |
Abstract: | Sepsis is a critical, life-threatening condition that demands precise prediction to mitigate adverse outcomes. The heterogeneity of sepsis leads to variable prognoses, making early and accurate identification increasingly difficult. Despite ongoing advancements, no single gold standard has emerged for sepsis prediction. Current research explores a range of prognostic tools, from traditional scoring systems and biomarkers to cutting-edge omics technologies and artificial intelligence. These tools can differ significantly across patient populations and clinical settings, such as the emergency department (ED) and intensive care unit (ICU). This review aims to critically evaluate the development and application of outcome prediction modalities in sepsis and other infectious diseases, highlighting the progress made and identifying areas for further research. Keywords: Outcome prediction, Infectious Disease, Sepsis |
URI: | http://localhost:8080/xmlui/handle/123456789/9681 |
Appears in Collections: | VOL 56 NO 4 2024 |
Files in This Item:
File | Description | Size | Format | |
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537-554.pdf | 1.23 MB | Adobe PDF | View/Open |
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