AIMC Topic: Vital Signs

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Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs.

Sensors (Basel, Switzerland)
Owing to accelerated societal aging, the prevalence of elderly individuals experiencing solitary or sudden death at home has increased. Therefore, herein, we aimed to develop a monitoring system that utilizes piezoelectric sensors for the non-invasiv...

Conventional and deep learning methods in heart rate estimation from RGB face videos.

Physiological measurement
Contactless vital signs monitoring is a fast-advancing scientific field that aims to employ monitoring methods that do not necessitate the use of leads or physical attachments to the patient in order to overcome the shortcomings and limits of traditi...

Remote Monitoring and Artificial Intelligence: Outlook for 2050.

Anesthesia and analgesia
Remote monitoring and artificial intelligence will become common and intertwined in anesthesiology by 2050. In the intraoperative period, technology will lead to the development of integrated monitoring systems that will integrate multiple data strea...

Prediction of Transfusion among In-patient Population using Temporal Pattern based Clinical Similarity Graphs.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Intelligent prediction of risk of blood transfusion among hospitalized patients can identify at-risk patients and provide timely information to the hospital to plan and reserve resources to meet the demand of blood transfusion. While previously propo...

Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning ...

Effectiveness of mobile robots collecting vital signs and radiation dose rate for patients receiving Iodine-131 radiotherapy: A randomized clinical trial.

Frontiers in public health
OBJECTIVE: Patients receiving radionuclide 131I treatment expose radiation to others, and there was no clinical trial to verify the effectiveness and safety of mobile robots in radionuclide 131I isolation wards. The objective of this randomized clini...

Development of a machine learning model for the prediction of the short-term mortality in patients in the intensive care unit.

Journal of critical care
PURPOSE: The aim of this study was to develop and evaluate a machine learning model that predicts short-term mortality in the intensive care unit using the trends of four easy-to-collect vital signs.

Prediction of serious outcomes based on continuous vital sign monitoring of high-risk patients.

Computers in biology and medicine
Continuous monitoring of high-risk patients and early prediction of severe outcomes is crucial to prevent avoidable deaths. Current clinical monitoring is primarily based on intermittent observation of vital signs and the early warning scores (EWS). ...

Camera fusion for real-time temperature monitoring of neonates using deep learning.

Medical & biological engineering & computing
The continuous monitoring of vital signs is a crucial aspect of medical care in neonatal intensive care units. Since cable-based sensors pose a potential risk for the immature skin of preterm infants, unobtrusive monitoring techniques using camera sy...