We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
Sekisui Diagnostics UK Ltd.

Download Mobile App




Machine Learning Programs Predict Mortality Risk by Analyzing Results from Routine Hospital Tests

By HospiMedica International staff writers
Posted on 22 Mar 2023
Print article
Image: Machine learning program can accurately predict a patient’s risk of death within a month, a year and five years (Photo courtesy of Pexels)
Image: Machine learning program can accurately predict a patient’s risk of death within a month, a year and five years (Photo courtesy of Pexels)

Individuals having high blood pressure or symptoms of heart disease, such as chest pain, shortness of breath or an irregular heartbeat generally visit a hospital or an emergency department. In such cases, a clinician usually orders an electrocardiogram, or ECG - a standard test in which tiny electrodes are taped to the chest for checking the heart’s rhythm and electrical activity. Hospital ECGs are mostly read by a doctor or nurse at the patient’s bedside, but now researchers are applying artificial intelligence (AI) to gather additional information from those results to improve patients care.

A research team at University of Alberta (Edmonton, Alberta, Canada) has developed and trained machine learning programs using a massive dataset of 1.6 million ECGs performed on 244,077 patients spanning over a period from 2007 till 2020. The algorithm predicted the risk of death from all causes within one month, one year, and five years with an impressive 85% accuracy rate, ranking the patients into one of five categories, ranging from the lowest to the highest risk. The algorithm's precision was substantially enhanced when demographic information such as age and sex, along with the results of six standard laboratory blood tests (creatinine, kidney function, sodium, troponin, hemoglobin, and potassium) were incorporated into the analysis.

This study serves as a proof-of-concept for utilizing routinely collected data to enhance individual care, enabling the healthcare system to “learn” on the go. The initial phase of the study examined ECG results of all the patients. However, the research team aims to refine these predictive models to cater to specific subgroups of patients. In the subsequent phases, the study will also focus on forecasting heart-related causes of death. The researchers highlight the immense advantage of employing high-powered computing as it can simultaneously view the patterns in a multitude of data points.

“These findings illustrate how machine learning models can be employed to convert data collected routinely in clinical practice to knowledge that can be used to augment decision-making at the point of care as part of a learning health-care system,” the researchers concluded in the study.

Related Links:
University of Alberta

Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
Soft-Tissues Biopsy Needle
MR-CLEAR

Print article

Channels

Critical Care

view channel
Image: The new risk assessment tool determines patient-specific risks of developing unfavorable outcomes with heart failure (Photo courtesy of 123RF)

Powerful AI Risk Assessment Tool Predicts Outcomes in Heart Failure Patients

Heart failure is a serious condition where the heart cannot pump sufficient blood to meet the body's needs, leading to symptoms like fatigue, weakness, and swelling in the legs and feet, and it can ultimately... Read more

Surgical Techniques

view channel
Image: The multi-sensing device can be implanted into blood vessels to help physicians deliver timely treatment (Photo courtesy of IIT)

Miniaturized Implantable Multi-Sensors Device to Monitor Vessels Health

Researchers have embarked on a project to develop a multi-sensing device that can be implanted into blood vessels like peripheral veins or arteries to monitor a range of bodily parameters and overall health status.... Read more

Patient Care

view channel
Image: The portable, handheld BeamClean technology inactivates pathogens on commonly touched surfaces in seconds (Photo courtesy of Freestyle Partners)

First-Of-Its-Kind Portable Germicidal Light Technology Disinfects High-Touch Clinical Surfaces in Seconds

Reducing healthcare-acquired infections (HAIs) remains a pressing issue within global healthcare systems. In the United States alone, 1.7 million patients contract HAIs annually, leading to approximately... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The Quantra Hemostasis System has received US FDA special 510(k) clearance for use with its Quantra QStat Cartridge (Photo courtesy of HemoSonics)

Critical Bleeding Management System to Help Hospitals Further Standardize Viscoelastic Testing

Surgical procedures are often accompanied by significant blood loss and the subsequent high likelihood of the need for allogeneic blood transfusions. These transfusions, while critical, are linked to various... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.