Tag: technology
Predicting SA-AKI Mortality Using XGBoost Algorithm
The integration of advanced techniques with the XGBoost algorithm yielded a highly accurate and interpretable model for predicting Sepsis-Associated Acute Kidney Injury (SA-AKI) mortality across diverse populations. It... read more
Machine Learning to Predict Extubation Success Using the SBT
Among the predictive models that used MOT, VCD, and the spontaneous breathing trial (SBT) as input variables through five machine learning techniques, decision trees and artificial neural networks demonstrated the best diagnostic... read more
ECMO in the Adult Patient (Core Critical Care)
Extracorporeal membrane oxygenation (ECMO) is developing rapidly, and is now part of the toolkit for the management of all patients with severe respiratory or cardiac failure. Clinicians of all disciplines are in need of... read more
Predicting MODS in Trauma-induced Sepsis with Machine Learning Models
The nomogram and machine learning models provide enhanced predictive accuracy for multiple organ dysfunction syndrome (MODS) in trauma-induced sepsis patients compared to traditional scoring systems. These tools, accessible... read more
Enhanced Prediction of ICU LOS Using a Stack Ensemble of Machine Learning Models
The Length of Stay (LoS) refers to the time between a patient's hospital admission and discharge. LoS is considered to increase as the complexity of the disease increases. A prolonged stay in the Intensive Care Unit (ICU)... read more
Rapid and Accurate Sepsis Identification Using AI
This study demonstrates that by providing streamlined predictions using CBC+DIFF data without requiring extensive clinical parameters, the AI-CDSS can be seamlessly integrated into clinical workflows, enhancing rapid, accurate... read more
Developing a Rapid Screening Tool for High-risk ICU Sepsis Patients
This study demonstrates that machine learning-based prediction models for sepsis can enhance clinical outcomes through accurate risk assessment during the critical first 24h of ICU admission. Despite healthcare system... read more
Should we use AI for writing ICU diaries? Yes!
AI-generated diaries for patients in intensive care units (ICU) may offer smart strategies to convince typical barriers against the use of ICU diaries in practice and have further benefits. However there are different arguments... read more
Hemodynamic Monitoring Made Easy: Mastering Critical Care Skills for Nurses and Clinicians
This thorough, simple-to-follow book was created especially for critical care nurses, clinicians, and other healthcare workers to help you fully utilize hemodynamic monitoring. With its straightforward, methodical approach... read more
Specialty Imaging: HRCT of the Lung
Part of the highly regarded Specialty Imaging series, HRCT of the Lung, third edition, reflects the many recent changes in HRCT diagnostic interpretation. An easy-to-read bulleted format and thousands of state-of-the-art... read more
Prediction of AKI in ICU Patients Based on Interpretable Machine Learning
The machine learning model described in this study is capable of accurately predicting the onset of AKI in ICU patients up to 24 hours in advance. Validated within the MIMIC-IV and MIMIC-III databases, the model demonstrates... read more
Does AI Close Gaps in Clinical Pharmacology in the ICU?
Artificial intelligence (AI) has the potential to close significant gaps in clinical pharmacology in the ICU. Its applications range from personalized dosing and predictive analytics to clinical trial optimization and education.... read more
Leveraging the Power of Routinely Collected ICU Data
With the steady advent of electronic health records, the abundance of routinely recorded data related to the treatment and care of intensive care unit (ICU) patients can be nothing short of overwhelming. Over tens of... read more
Extubation Failure Risk Prediction Model Using Bedside Ultrasound
We have established a risk prediction model for extubation failure in mechanically ventilated ICU patients. This risk model base on bedside ultrasound parameters provides valuable insights for identifying high-risk patients... read more
Predicting ICU Admission in COVID-19-Infected Pregnant Women Using Machine Learning
Routinely collected clinical and laboratory data of COVID-19-infected pregnant women may help recognize high-risk groups who are more liable for complications and more severe course or prognosis and require an ICU admission.... read more
Prediction of Prolonged Mechanical Ventilation in the ICU via Machine Learning
Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for early clinical interventions, prevention of secondary complications such as nosocomial infections, and effective triage of hospital... read more
An Optimal Antibiotic Selection Framework for Sepsis Patients Using AI
In this work we present OptAB, the first completely data-driven online-updateable antibiotic selection model based on Artificial Intelligence (AI) for Sepsis patients accounting for side-effects. OptAB performs an iterative... read more
AI Interpretation of Chest Radiographs in the ICU. Ready for Prime Time?
Chest radiography (CXR) is the primary tool to visualize thoracic pathologies in the ICU. However, accurate interpretation of CXRs is challenging, even when imaging is performed under optimal conditions. This results... read more
Improving Patient Outcomes: Sepsis Protocols and Rapid Host Response Technologies
Patients come into the emergency department (ED) with symptoms, not diagnoses. That’s when time is of the essence. Clinicians must quickly triage patients and establish an appropriate care pathway to obtain the best possible... read more
Army Scientists’ Technique for Early Sepsis Detection in Burn Patients Submitted to FDA
A new invention developed at the U.S. Army Medical Research and Development Command uses an artificial intelligence machine learning algorithm to identify whether burn patients are at risk of experiencing life-threatening... read more
Sepsis Mortality Prediction in ICU Patients Using Machine Learning
This study has achieved significant advancements in predicting sepsis outcomes by utilizing advanced machine learning techniques and sophisticated data preprocessing methods. These methods include data grouping and effective... read more
AI to Predicting Mortality Risk in ICU Patients with AKI
To address the limitations of early acute kidney injury (AKI) prediction, researchers have increasingly turned to machine learning methods. However, the success of these models hinges on the selection of relevant features.... read more
