AIMC Topic: Vital Signs
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DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.
Sensors (Basel, Switzerland)
Apr 23, 2024
Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection a...
Application of artificial intelligence technology in monitoring students' health: Preliminary results of Syiah Kuala Integrated Medical Monitoring (SKIMM).
Narra J
Apr 17, 2024
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...
Ballistocardial Signal-Based Personal Identification Using Deep Learning for the Non-Invasive and Non-Restrictive Monitoring of Vital Signs.
Sensors (Basel, Switzerland)
Apr 15, 2024
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...
Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department.
The American journal of emergency medicine
Mar 16, 2024
OBJECTIVE: To develop and externally validate models based on neural networks and natural language processing (NLP) to identify suspected serious infections in emergency department (ED) patients afebrile at initial presentation.
Conventional and deep learning methods in heart rate estimation from RGB face videos.
Physiological measurement
Feb 9, 2024
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
Jan 12, 2024
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
Jan 11, 2024
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
Apr 29, 2023
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
Jan 9, 2023
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
Jul 11, 2022
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.
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