AIMC Topic: Sepsis

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Development and temporal validation of a nomogram for predicting ICU 28-day mortality in middle-aged and elderly sepsis patients: An eICU database study.

PloS one
BACKGROUND AND OBJECTIVE: Despite advances in intensive care, sepsis remains a leading cause of mortality in intensive care unit (ICU) patients, especially middle-aged and elderly individuals. Given the limitations of conventional scoring systems and...

Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.

Scientific reports
This study aims to establish and validate prediction models based on novel machine learning (ML) algorithms for augmented renal clearance (ARC) in critically ill patients with sepsis. Patients with sepsis were extracted from the Medical Information M...

AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.

Science advances
With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Given the potential of hyperspectral imaging (HSI) to monitor microcircu...

Development of a risk prediction model for sepsis-related delirium based on multiple machine learning approaches and an online calculator.

PloS one
BACKGROUND: Sepsis-associated delirium (SAD) occurs due to disruptions in neurotransmission linked to inflammatory responses from infections. It poses significant challenges in clinical management and is associated with poor outcomes. Survivors often...

Identification of necroptotic biomarkers associated with immune microenvironment in sepsis based on the protein-protein interaction network and machine learning.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Necroptosis is inflammatorily sparked and closely associated with sepsis, but the crosstalk between necroptosis and inflammation in sepsis has rarely been studied in depth. This study is designed to reveal the role of necroptosis in the p...

Mortality and antibiotic timing in deep learning-derived surviving sepsis campaign risk groups: a multicenter study.

Critical care (London, England)
BACKGROUND: The current Surviving Sepsis Campaign (SSC) guidelines provide recommendations on timing of administering antibiotics in sepsis patients based on probability of sepsis and presence of shock. However, there have been minimal efforts to str...

Identifying propionate metabolism-related genes as biomarkers of sepsis development and therapeutic targets.

Scientific reports
The treatment of sepsis is challenging due to unclear mechanisms. Propionate is increasingly seen as critical to sepsis pathophysiology by bridging gut microbiota and immunity, but the mechanisms remain unclear. Our study analysed differences in prop...

Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks.

PloS one
PURPOSE: Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. As show...

5-Hydroxymethylcytosine signatures as diagnostic biomarkers for septic cardiomyopathy.

Scientific reports
At present, there are currently no molecular biomarkers for the early diagnosis of sepsis cardiomyopathy (SCM) in clinical practice. This study focuses on an in-depth examination of the DNA hydroxymethylation profiles within plasma extracellular vesi...

Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling.

Scientific reports
Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rates and complex patient responses. Accurately predicting patient outcomes is essential for optimizing...