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Vital Signs

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Application of artificial intelligence technology in monitoring students' health: Preliminary results of Syiah Kuala Integrated Medical Monitoring (SKIMM).

Narra J
Health promoting university is a holistic approach to health that uses higher education settings to create a learning environment that improves the health and well-being of the campus community in a sustainable manner. The utilization of technology s...

E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing.

Sensors (Basel, Switzerland)
Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-s...

Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study.

Journal of medical systems
BACKGROUND:  Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalations (UCE) following post-anesthesia care unit (PACU) discharge. Studies indicate patient risk factors ...

Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data.

IEEE journal of biomedical and health informatics
The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed...

A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.

Annals of laboratory medicine
BACKGROUND: Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles...

Interpretable machine learning for predicting sepsis risk in emergency triage patients.

Scientific reports
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive tr...

A comparative study of neural network architectures for vital signs monitoring based on the national early warning systems (NEWS).

Health informatics journal
The study aims to assess the efficacy of various neural network architectures in predicting the National Early Warning Systems (NEWS) score, using vital signs, to enhance early warning and monitoring in clinical settings. A comparative evaluation o...

Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data.

BMC emergency medicine
BACKGROUND: Critically ill patients can deteriorate rapidly; therefore, prompt prehospital interventions and seamless transition to in-hospital care upon arrival are crucial for improving survival. In Japan, helicopter emergency medical services (HEM...