BACKGROUND AND OBJECTIVE: Elderly patients with Chronic obstructive pulmonary disease (COPD) and respiratory failure admitted to the intensive care unit (ICU) have a poor prognosis, and the occurrence of delirium further worsens outcomes and increase...
Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
40109460
OBJECTIVE: Heart failure (HF) complicated by acute kidney injury (AKI) significantly impacts patient outcomes, and it is crucial to make early predictions of short-term mortality. This study is focused on developing an interpretable machine learning ...
BACKGROUND: Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to develop and validate a machine learning (ML) model to predict SIC risk in patients with sepsis.
BMC medical informatics and decision making
40155935
BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims to construct a mortality prediction model for patients with SICH using four various artificial ...
OBJECTIVE: This study aims to construct and compare four machine learning models using the MIMIC-IV database to identify high-risk factors for multidrug-resistant organism (MDRO) infection in Ventilator-associated pneumonia (VAP) patients.
BACKGROUND: Interest in integrating robotics within intensive care units (ICUs) has been propelled by technological advancements, workforce challenges, and heightened clinical demands, including during the COVID-19 pandemic. The integration of roboti...
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...
BACKGROUND: Early detection of clinical deterioration using machine-learning early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective validation, and were not t...
AIM: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. ...
pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and reducing healthcare costs. This study aims to develop a predictive model using machine learning techn...