AIMC Topic: Vital Signs
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Kinematics approach with neural networks for early detection of sepsis (KANNEDS).
BMC medical informatics and decision making
May 20, 2021
BACKGROUND: Sepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes. In recent years, researchers have used models to classify positive patients or identify the probab...
Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives.
Biosensors
Apr 13, 2021
According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly because a large amount of patients do not have any symptoms. However, traditional cuff measurement ...
A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients.
Sensors (Basel, Switzerland)
Feb 21, 2021
Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to conti...
Physical Features and Vital Signs Predict Serum Albumin and Globulin Concentrations Using Machine Learning.
Asian Pacific journal of cancer prevention : APJCP
Feb 1, 2021
OBJECTIVE: Serum protein concentrations are diagnostically and prognostically valuable in cancer and other diseases, but their measurement via blood test is uncomfortable, inconvenient, and costly. This study investigates the possibility of predictin...
Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system.
Scientific reports
Dec 9, 2020
Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortali...
Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach.
Scientific reports
Dec 4, 2020
Mortality risk prediction can greatly improve the utilization of resources in intensive care units (ICUs). Existing schemes in ICUs today require laborious manual input of many complex parameters. In this work, we present a scheme that uses variation...
SSP: Early prediction of sepsis using fully connected LSTM-CNN model.
Computers in biology and medicine
Nov 10, 2020
BACKGROUND: Sepsis is a life-threatening condition that occurs due to the body's reaction to infections, and it is a leading cause of morbidity and mortality in hospitals. Early prediction of sepsis onset facilitates early interventions that promote ...
Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study.
PloS one
Jul 13, 2020
BACKGROUND: Although machine learning-based prediction models for in-hospital cardiac arrest (IHCA) have been widely investigated, it is unknown whether a model based on vital signs alone (Vitals-Only model) can perform similarly to a model that cons...
Prediction of mortality in Intensive Care Units: a multivariate feature selection.
Journal of biomedical informatics
May 23, 2020
CONTEXT: The critical nature of patients in Intensive Care Units (ICUs) demands intensive monitoring of their vital signs as well as highly qualified professional assistance. The combination of these needs makes ICUs very expensive, which requires in...
Contactless Real-Time Heartbeat Detection via 24 GHz Continuous-Wave Doppler Radar Using Artificial Neural Networks.
Sensors (Basel, Switzerland)
Apr 21, 2020
The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from sig...
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