AIMC Topic: Sepsis

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Machine learning reveals ferroptosis features and a novel ferroptosis classifier in patients with sepsis.

Immunity, inflammation and disease
OBJECTIVE: Sepsis is an organ malfunction disease that may become fatal and is commonly accompanied by severe complications such as multiorgan dysfunction. Patients who are already hospitalized have a high risk of death due to sepsis. Even though ear...

[Construction of a back propagation neural network model for predicting urosepsis after flexible ureteroscopic lithotripsy].

Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences
OBJECTIVES: To analyze the association of serum heparin-binding protein (HBP) and C-reactive protein (CRP) levels with urosepsis following flexible ureteroscopic lithotripsy (FURL) and to construct a back propagation neural network prediction model.

Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Early identification of sepsis has been shown to significantly improve patient prognosis.

[Research progress of artificial intelligence technology in early diagnosis of sepsis].

Zhonghua wei zhong bing ji jiu yi xue
Sepsis is caused by infection, which can ultimately lead to multiple organ dysfunction and even life-threatening. Early recognition and early treatment can significantly improve the prognosis of sepsis patients. However, the effect of using a single ...

Evidence from Machine Learning, Diagnostic Hub Genes in Sepsis and Diagnostic Models based on Xgboost Models, Novel Molecular Models for the Diagnosis of Sepsis.

Current medicinal chemistry
BACKGROUND: Systemic multi-organ dysfunction resulting from dysregulated immune responses in the host triggered by microbial infection or other factors is a major cause of death in sepsis, and secretory pathways play an important role in it.

ExpertNet: A Deep Learning Approach to Combined Risk Modeling and Subtyping in Intensive Care Units.

IEEE journal of biomedical and health informatics
Risk models play a crucial role in disease prevention, particularly in intensive care units (ICUs). Diseases often have complex manifestations with heterogeneous subpopulations, or subtypes, that exhibit distinct clinical characteristics. Risk models...

Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To retrieve and appraise studies of deployed artificial intelligence (AI)-based sepsis prediction algorithms using systematic methods, identify implementation barriers, enablers, and key decisions and then map these to a novel end-to-end c...

An interpretable machine learning model for real-time sepsis prediction based on basic physiological indicators.

European review for medical and pharmacological sciences
OBJECTIVE: In view of the important role of risk prediction models in the clinical diagnosis and treatment of sepsis, and the limitations of existing models in terms of timeliness and interpretability, we intend to develop a real-time prediction mode...

Introducing "Viewpoint: Turning the Air Blue".

American journal of respiratory and critical care medicine