AIMC Topic: Critical Care

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Artificial Intelligence: its Future and Impact on Acute Medicine.

Acute medicine
This commentary explores the potential impact of artificial intelligence (AI) in acute medicine, considering its possibilities and challenges. With its ability to simulate human intelligence, AI holds the promise for supporting timely decision-making...

Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.

Renal failure
PURPOSE: Acute kidney injury (AKI) is a common complication and associated with a poor clinical outcome. In this study, we developed and validated a model for predicting the risk of AKI through machine learning methods in critical care patients with ...

Clinical utility of automatic phenotype annotation in unstructured clinical notes: intensive care unit use.

BMJ health & care informatics
OBJECTIVE: Clinical notes contain information that has not been documented elsewhere, including responses to treatment and clinical findings, which are crucial for predicting key outcomes in patients in acute care. In this study, we propose the autom...

Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.

Advances in chronic kidney disease
Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as ...

Point-of-Care Ultrasound in the Intensive Care Unit: Applications, Limitations, and the Evolution of Clinical Practice.

Clinics in chest medicine
The use of point-of-care ultrasonography in the intensive care unit has been rapidly advancing over the past 20 years. This review will provide a broad overview of the discipline spanning lung ultrasonography to advanced critical care echocardiograph...

Development and validation of a deep learning model to predict the survival of patients in ICU.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to im...

[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.

Considerations for the implementation of machine learning into acute care settings.

British medical bulletin
INTRODUCTION: Management of patients in the acute care setting requires accurate diagnosis and rapid initiation of validated treatments; therefore, this setting is likely to be an environment in which cognitive augmentation of the clinician's provisi...

Developing a Data Driven Approach for Early Detection of SIRS in Pediatric Intensive Care Using Automatically Labeled Training Data.

Studies in health technology and informatics
Critical care can benefit from analyzing data by machine learning approaches for supporting clinical routine and guiding clinical decision-making. Developing data-driven approaches for an early detection of systemic inflammatory response syndrome (SI...