AIMC Topic: Intensive Care Units

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Machine Learning-Based Mortality Prediction for Acute Gastrointestinal Bleeding Patients Admitted to Intensive Care Unit.

Current medical science
OBJECTIVE: The study aimed to develop machine learning (ML) models to predict the mortality of patients with acute gastrointestinal bleeding (AGIB) in the intensive care unit (ICU) and compared their prognostic performance with that of Acute Physiolo...

Prioritisation of functional needs for ICU intelligent robots in China: a consensus study based on the national survey and nominal group technique.

BMJ open
OBJECTIVE: This study aims to define the prioritisation of the needs for an intelligent robot's functions in the intensive care unit (ICU) from a clinical perspective.

Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model.

JMIR medical informatics
BACKGROUND: Agitation and sedation management is critical in intensive care as it affects patient safety. Traditional nursing assessments suffer from low frequency and subjectivity. Automating these assessments can boost intensive care unit (ICU) eff...

Convolutional long short-term memory neural network integrated with classifier in classifying type of asynchrony breathing in mechanically ventilated patients.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Asynchronous breathing (AB) occurs when a mechanically ventilated patient's breathing does not align with the mechanical ventilator (MV). Asynchrony can negatively impact recovery and outcome, and/or hinder MV management. A ...

Impact of Sepsis Onset Timing on All-Cause Mortality in Acute Pancreatitis: A Multicenter Retrospective Cohort Study.

Journal of intensive care medicine
BackgroundSepsis complicates acute pancreatitis (AP), increasing mortality risk. Few studies have examined how sepsis and its onset timing affect mortality in AP. This study evaluates the association between sepsis occurrence and all-cause mortality ...

Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.

PloS one
OBJECTIVES: Developing and validating interpretable machine learning (ML) models for predicting whether triaged patients need to be admitted to the intensive care unit (ICU).

An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.

BMC medical informatics and decision making
BACKGROUND: Identifying key variables is essential for developing clinical outcome prediction models based on high-dimensional electronic medical records (EMR). However, despite the abundance of feature selection (FS) methods available, challenges re...

Enhancing machine learning performance in cardiac surgery ICU: Hyperparameter optimization with metaheuristic algorithm.

PloS one
The healthcare industry is generating a massive volume of data, promising a potential goldmine of information that can be extracted through machine learning (ML) techniques. The Intensive Care Unit (ICU) stands out as a focal point within hospitals a...