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Critical Care

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Dynamic Sepsis Prediction for Intensive Care Unit Patients Using XGBoost-Based Model With Novel Time-Dependent Features.

IEEE journal of biomedical and health informatics
Sepsis is a systemic inflammatory response caused by pathogens such as bacteria. Because its pathogenesis is not clear, the clinical manifestations of patients vary greatly, and the alarming incidence and mortality pose a great threat to patients and...

Phenotypes of sickle cell intensive care admissions: an unsupervised machine learning approach in a single-center retrospective cohort.

Annals of hematology
Sickle cell disease (SCD) is associated with multiple known complications and increased mortality. This study aims to further understand the profile of intensive care unit (ICU) admissions of SCD patients. In this single-center retrospective cohort (...

Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers.

PloS one
The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not routinely captured in structured electronic health records. This study sought to develop and validate...

[The use of robotic and technical systems for early mobilization of intensive care patients: A scoping review].

Pflege
The use of robotic and technical systems for early mobilization of intensive care patients: A scoping review Intensive care patients are often subjected to immobility for too long. However, when they are mobilized early, positive effects on patient...

Tell me something interesting: Clinical utility of machine learning prediction models in the ICU.

Journal of biomedical informatics
In recent years, extensive resources are dedicated to the development of machine learning (ML) based clinical prediction models for intensive care unit (ICU) patients. These models are transforming patient care into a collaborative human-AI task, yet...

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

BMC medical imaging
BACKGROUND: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission.

Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients.

BioMed research international
To observe the clinical efficacy of early enteral nutrition application in critically ill neurosurgical patients, in this paper, we have developed a prediction model for enteral nutrition support in neurosurgical intensive care patients which is prim...

Use of Robots in Critical Care: Systematic Review.

Journal of medical Internet research
BACKGROUND: The recent focus on the critical setting, especially with the COVID-19 pandemic, has highlighted the need for minimizing contact-based care and increasing robotic use. Robotics is a rising field in the context of health care, and we sough...

Modern Learning from Big Data in Critical Care: Primum Non Nocere.

Neurocritical care
Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in ...