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





AI Model for Monitoring COVID-19 Predicts Mortality Within First 30 Days of Admission

By HospiMedica International staff writers
Posted on 08 Apr 2022
Print article
Image: A new study has set an international benchmark in applying AI for monitoring and managing COVID-19 (Photo courtesy of Unsplash)
Image: A new study has set an international benchmark in applying AI for monitoring and managing COVID-19 (Photo courtesy of Unsplash)

A study by a team of researchers has become an international benchmark for the reliable use of artificial intelligence (AI) in monitoring and managing COVID-19.

In an article published in the Journal of the American Medical Informatics Association, the research team at the Universitat Politècnica de València (UPV, Valencia, Spain) has demonstrated the limitations that the variability or heterogeneity of data may have in reliably applying AI when it comes from multiple sources, e.g. a range of hospitals or countries. The article sets out the key aspects of potential solutions to such limitations. Furthermore, the team has developed new tools based on this study to help describe and classify patients with COVID-19.

The researchers have also developed an AI model for the early prediction of mortality (within the first 30 days of admission to the emergency department), focusing principally on adults aged over 50. They have also developed a deep learning application that helps to predict severity in all age groups, with the advantage of being able to operate even with incomplete patient information, offering robust and reliable AI in the event of data quality issues.

“These predictive models can help to select the best treatment for each patient according to their mortality risk, and to plan and manage resources in cases of low availability of resources, and in a way that can withstand potential uncertainties in the available information,” said Carlos Sáez, a member of the BDSLab-ITACA group research team at Universitat Politècnica de València, who coordinated the study.

In addition, following a study of nearly 800,000 COVID-19 cases, the researchers have developed a new technique to investigate subphenotypes (dividing patient populations into meaningful groups) in line with clinical characteristics. This technique, based on meta-clustering exploratory AI, can be used to automatically obtain a large number of results at different socio-demographic levels (by age group, gender, and combinations thereof), which would otherwise have to be carried out manually, involving additional work. This technique not only encourages non-discrimination, but also presents the results to the user in a detailed and intuitive manner, ready for exploration. Applying this technique to the cases led the team to conclude that chronological age alone cannot be used as a risk factor for severity, but rather must always be accompanied by comorbidities and even habits (physiological age).

“We also observed that, under equivalent clinical conditions, women have a higher recovery rate than men and, among older people, it is those aged over 100 who recover best. And we found that there is significant variability in recovery rates between different states in Mexico and also depending on the clinical institution,” concluded Carlos Sáez.

Related Links:
Universitat Politècnica de València 

Gold Member
Real-Time Diagnostics Onscreen Viewer
GEMweb Live
Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Silver Member
Wireless Mobile ECG Recorder
NR-1207-3/NR-1207-E
New
X-Ray QA Meter
Piranha CT

Print article

Channels

Surgical Techniques

view channel
Image: Miniaturized electric generators based on hydrogels for use in biomedical devices (Photo courtesy of HKU)

Hydrogel-Based Miniaturized Electric Generators to Power Biomedical Devices

The development of engineered devices that can harvest and convert the mechanical motion of the human body into electricity is essential for powering bioelectronic devices. This mechanoelectrical energy... Read more

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... 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.