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Shock

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Low-soluble TREM-like transcript-1 levels early after severe burn reflect increased coagulation disorders and predict 30-day mortality.

Burns : journal of the International Society for Burn Injuries
BACKGROUND: Patients with severe burns often show systemic coagulation changes in the early stage and even develop extensive coagulopathy. Previous studies have confirmed that soluble TREM-like transcript-1 (sTLT-1) mediates a novel mechanism of haem...

Impairment of Thyroid Function in Critically Ill Patients in the Intensive Care Units.

The American journal of the medical sciences
Unexplained hypotension in the intensive care unit is commonly attributed to volume depletion, cardiorespiratory failure, sepsis, or relative adrenal insufficiency. In these acute conditions, thyroid hormone levels measured in blood, serum or plasma ...

[Advances in the research of application of artificial intelligence in burn field].

Zhonghua shao shang za zhi = Zhonghua shaoshang zazhi = Chinese journal of burns
Artificial intelligence has been able to automatically learn and judge large-scale data to some extent. Based on database of a large amount of burn data and in-depth learning, artificial intelligence can assist burn surgeons to evaluate burn surface,...

Early prediction of circulatory failure in the intensive care unit using machine learning.

Nature medicine
Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monit...

Validating clinical threshold values for a dashboard view of the compensatory reserve measurement for hemorrhage detection.

The journal of trauma and acute care surgery
BACKGROUND: Compensatory reserve measurement (CRM) is a novel noninvasive monitoring technology designed to assess physiologic reserve using feature interrogation of arterial pulse waveforms. This study was conducted to validate clinically relevant C...

Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: To create a deep learning algorithm capable of video classification, using a long short-term memory (LSTM) network, to analyze collapsibility of the inferior vena cava (IVC) to predict fluid responsiveness in critically ill patients.

Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care gro...

Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock.

Military medicine
INTRODUCTION: The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers' ability to treat patients create an urgent need for effective tools to support care. We report on a project to develo...

Development of a field artificial intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds.

The journal of trauma and acute care surgery
BACKGROUND: In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome ...

CLEAR-Shock: Contrastive LEARning for Shock.

IEEE journal of biomedical and health informatics
Shock is a life-threatening condition characterized by generalized circulatory failure, which can have devastating consequences if not promptly treated. Thus, early prediction and continuous monitoring of physiological signs are essential for timely ...