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WhatsApp: +86 18221755073Background: Acute kidney injury (AKI) is a common complication in critically ill patients with sepsis and is often associated with a poor prognosis. We aimed to construct and validate an interpretable prognostic prediction model for patients with sepsis-associated AKI (S-AKI) using machine learning (ML) methods.
WhatsApp: +86 18221755073Objective: To predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches. Methods: Data were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for …
WhatsApp: +86 18221755073Background: Few studies have developed automatic systems for identifying social distress, spiritual pain, and severe physical and phycological symptoms from text data in electronic medical records. Aim: To develop models to detect social distress, spiritual pain, and severe physical and psychological symptoms in terminally ill patients with cancer from unstructured text data …
WhatsApp: +86 18221755073Background: Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP.
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WhatsApp: +86 18221755073Introduction: The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship between medication regimen complexity and patient outcomes.
WhatsApp: +86 18221755073A product's mesh size of 100 means that it has been through a screen with 100 openings per square inch. It represents the roughly average or mean particle size for that grade. What is the meaning of 20 mesh size?
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WhatsApp: +86 18221755073Our real-time machine learning model to predict short-term mortality in critically ill patients showed excellent performance in both internal and external validations. This model could be a useful decision-support tool in the intensive care units to assist clinicians.
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WhatsApp: +86 18221755073Background: New-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML).
WhatsApp: +86 18221755073The specifications of standard sieves vary from country to country. The commonly used Taylor system uses the number of holes per inch as the sieve number, which is called mesh. For example, a 100 mesh sieve means there are 100 …
WhatsApp: +86 18221755073interpretable machine learning for predicting risk of invasive fungal infection in critically ill patients in the intensive care unit: a retrospective cohort study based on mimic-iv database Shock . 2024 Jun 1;61(6):817-827. doi: 10.1097/SHK.0000000000002312.
WhatsApp: +86 18221755073Is it possible to identify where are the "ill-shaped tets" in the mesh? I'm having the following error: "Warning : 156 ill-shaped tets are still in the mesh Warning : Surface mesh: worst distortion" I can't improve it by changing the density value. I'm thinking about simplify my geometry but I don't know where is the problem. Thanks.
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WhatsApp: +86 18221755073Objectives: This study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms. Methods: Patients who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to …
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