AIMC Topic: Sepsis

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Predicting mortality and risk factors of sepsis related ARDS using machine learning models.

Scientific reports
Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic. Accurate prediction of in-hospital mortality of patients is crucial to optimize treatment and improve prognosis under the new global definition of AR...

Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.

Frontiers in cellular and infection microbiology
BACKGROUND: Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to the symptom similarity with other neurological disorders and the absence of specific biomarkers, ear...

CLIC1 and IFITM2 expression in brain tissue correlates with cognitive impairment via immune dysregulation in sepsis and Alzheimer's disease.

International immunopharmacology
BACKGROUND: Sepsis, a life-threatening condition driven by dysregulated host responses to infection, is associated with long-term cognitive impairments resembling Alzheimer's disease (AD). However, the molecular mechanisms linking sepsis-induced cogn...

Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning.

Scientific reports
Sepsis is a highly variable condition, and tracking leukocyte patterns may offer insights for tailored treatment and prognosis. We used the MIMIC-IV database to analyze patients diagnosed with Sepsis-3 within 24 h of ICU admission. Latent class mixed...

Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated delirium.

PloS one
This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithms. On the whole, a cohort of 3,197 SAD patients were collected from the Medical Information Mart for...

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

Nature medicine
The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pr...

Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database.

BMC medical informatics and decision making
BACKGROUND: Sepsis, a severe systemic response to infection, frequently results in adverse outcomes, underscoring the urgency for prompt and accurate prognostic tools. Machine learning methods such as logistic regression, random forests, and CatBoost...

Constructing an early warning model for elderly sepsis patients based on machine learning.

Scientific reports
Sepsis is a serious threat to human life. Early prediction of high-risk populations for sepsis is necessary especially in elderly patients. Artificial intelligence shows benefits in early warning. The aim of the study was to construct an early machin...

Large Language Model-Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases: Development and Usability Study.

Journal of medical Internet research
BACKGROUND: Sepsis is a complex, life-threatening condition characterized by significant heterogeneity and vast amounts of unstructured data, posing substantial challenges for traditional knowledge graph construction methods. The integration of large...

Monocyte distribution width (MDW) as a reliable diagnostic biomarker for sepsis in patients with HIV.

Emerging microbes & infections
Sepsis is a leading cause of death among patients with HIV, but early diagnosis remains a challenge. This study evaluates the diagnostic performance of monocyte distribution width (MDW) in detecting sepsis in patients with HIV. A prospective observat...