AIMC Topic: Intensive Care Units

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Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features.

Critical care medicine
OBJECTIVES: Early prediction of sepsis is of utmost importance to provide optimal care at an early stage. This work aims to deploy soft-computing and machine learning techniques for early prediction of sepsis.

Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records.

Critical care medicine
OBJECTIVES: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for se...

Artificial intelligence to guide management of acute kidney injury in the ICU: a narrative review.

Current opinion in critical care
PURPOSE OF REVIEW: Acute kidney injury (AKI) frequently complicates hospital admission, especially in the ICU or after major surgery, and is associated with high morbidity and mortality. The risk of developing AKI depends on the presence of preexisti...

Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission.

Annals of surgery
OBJECTIVE: To compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the predict...

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.

Critical care medicine
OBJECTIVES: Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial inte...

A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care.

Critical care medicine
OBJECTIVES: As a life-threatening condition, sepsis is one of the major public health issues worldwide. Early prediction can improve sepsis outcomes with appropriate interventions. With the PhysioNet/Computing in Cardiology Challenge 2019, we aimed t...

A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit.

The journal of trauma and acute care surgery
BACKGROUND: Trauma patients admitted to critical care are at high risk of mortality because of their injuries. Our aim was to develop a machine learning-based model to predict mortality using Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework. We...

Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit.

The American journal of gastroenterology
INTRODUCTION: Acute gastrointestinal (GI) bleed is a common reason for hospitalization with 2%-10% risk of mortality. In this study, we developed a machine learning (ML) model to calculate the risk of mortality in intensive care unit patients admitte...

Machine Learning Prognostic Models for Gastrointestinal Bleeding Using Electronic Health Record Data.

The American journal of gastroenterology
Risk assessment tools for patients with gastrointestinal bleeding may be used for determining level of care and informing management decisions. Development of models that use data from electronic health records is an important step for future deploym...

Advances in the rehabilitation of intensive care unit acquired weakness: A case report on the promising use of robotics and virtual reality coupled to physiotherapy.

Medicine
INTRODUCTION: Traditional physiotherapy is currently the best approach to manage patients with intensive care unit acquired weakness (ICUAW). We report on a patient with ICUAW, who was provided with an intensive, in-patient regimen, that is, conventi...