AIMC Topic: Intensive Care Units

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[Survey and analysis on digital construction of primary hospitals in Guizhou Province].

Zhonghua wei zhong bing ji jiu yi xue
OBJECTIVE: To investigate the utilization status and awareness of digital hospital construction among medical staff in critical care department of primary hospitals, so as to promote the process of digital medical health.

Treatment Prediction in the ICU Setting Using a Partitioned, Sequential Deep Time Series Analysis.

Studies in health technology and informatics
We developed a neural network architecture to evaluate the patient's state using temporal data, patient's demographics and comorbidities. We examined the model's ability to predict both a binary medication-treatment decision and its specific dose in ...

Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records.

Studies in health technology and informatics
OBJECTIVE: We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children.

Validation of a Machine Learning Model for Early Shock Detection.

Military medicine
OBJECTIVES: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity...

Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond.

Critical care clinics
Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care un...

[Predicting prolonged length of intensive care unit stay machine learning].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
OBJECTIVE: To construct length of intensive care unit (ICU) stay (LOS-ICU) prediction models for ICU patients, based on three machine learning models support vector machine (SVM), classification and regression tree (CART), and random forest (RF), and...

Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury.

Current opinion in critical care
PURPOSE OF REVIEW: Acute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of th...

An Interpretable Intensive Care Unit Mortality Risk Calculator.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Mortality risk is a major concern to patients who have just been discharged from the intensive care unit (ICU). Many studies have been directed to construct machine learning models to predict such risk. Although these models are highly accurate, they...

Detecting Uncertainty of Mortality Prediction Using Confident Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Early mortality prediction is an actively researched problem that has led to the development of various severity scores and machine learning (ML) models for accurate and reliable detection of mortality in severely ill patients staying in intensive ca...

Predicting brain function status changes in critically ill patients via Machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In intensive care units (ICUs), a patient's brain function status can shift from a state of acute brain dysfunction (ABD) to one that is ABD-free and vice versa, which is challenging to forecast and, in turn, hampers the allocation of hosp...