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





Scientists Use Machine Learning Algorithm to Identify Six Types of COVID-19 with Distinctive Symptoms

By HospiMedica International staff writers
Posted on 20 Jul 2020
Print article
Image: SARS-CoV-2 (Photo courtesy of NIAID)
Image: SARS-CoV-2 (Photo courtesy of NIAID)
An analysis of data from the COVID Symptom Study app has revealed that there are six distinct ‘types’ of COVID-19, each distinguished by a particular cluster of symptoms and differing in the severity of the disease as well as need for respiratory support during hospitalization.

The findings have major implications for clinical management of COVID-19, and could help doctors predict who is most at risk and likely to need hospital care in a second wave of coronavirus infections. Although continuous cough, fever and loss of smell (anosmia) are usually highlighted as the three key symptoms of COVID-19, data gathered from app users shows that people can experience a wide range of different symptoms including headaches, muscle pains, fatigue, diarrhea, confusion, loss of appetite, shortness of breath and more. The progression and outcomes also vary significantly between people, ranging from mild flu-like symptoms or a simple rash to severe or fatal disease.

To find out whether particular symptoms tend to appear together and how this related to the progression of the disease, the research team at King’s College London (London, UK) used a machine learning algorithm to analyze data from a subset of around 1,600 users in the UK and US with confirmed COVID-19 who had regularly logged their symptoms using the app in March and April. The analysis revealed six specific groupings of symptoms emerging at characteristic timepoints in the progression of the illness, representing six distinct ‘types’ of COVID-19. The algorithm was then tested by running it on a second independent dataset of 1,000 users in the UK, US and Sweden, who had logged their symptoms during May. All people reporting symptoms experienced headache and loss of smell, with varying combinations of additional symptoms at various times. Some of these, such as confusion, abdominal pain and shortness of breath, are not widely known as COVID-19 symptoms, yet are hallmarks of the most severe forms of the disease.

The team also discovered that people experiencing particular symptom clusters were more likely to require breathing support in the form of ventilation or additional oxygen. The researchers then developed a model combining information about age, sex, BMI and pre-existing conditions together with symptoms gathered over just five days from the onset of the illness. This was able to predict which cluster a patient falls into and their risk of requiring hospitalization and breathing support with a higher likelihood of being correct than an existing risk model based purely on age, sex, BMI and pre-existing conditions alone. Given that most people who require breathing support come to hospital around 13 days after their first symptoms, this extra eight days represents a significant ‘early warning’ as to who is most likely to need more intensive care.

“These findings have important implications for care and monitoring of people who are most vulnerable to severe COVID-19,” said Dr Claire Steves from King’s College London. “If you can predict who these people are at day five, you have time to give them support and early interventions such as monitoring blood oxygen and sugar levels, and ensuring they are properly hydrated - simple care that could be given at home, preventing hospitalizations and saving lives.”

“Being able to gather big datasets through the app and apply machine learning to them is having a profound impact on our understanding of the extent and impact of COVID-19, and human health more widely,” said Sebastien Ourselin, professor of healthcare engineering at King’s College London and senior author of the study.

Related Links:
King’s College London

Gold Member
Real-Time Diagnostics Onscreen Viewer
GEMweb Live
Gold Member
12-Channel ECG
CM1200B
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
ICU Bed with Integrated Mattress
Activ8 Vivo

Print article

Channels

Critical Care

view channel
Image: The largest scale analysis compared longer-term percutaneous devices for aortic valve replacement versus surgery (Photo courtesy of Adobe Stock)

Transcatheter Valve Replacement Outcomes Similar To Surgery, Finds Study

A new study has shown that a minimally invasive procedure for replacing the aortic valve in the heart—known as transcatheter aortic valve replacement (TAVR)—is on par with the more traditional surgical... Read more

Surgical Techniques

view channel
Image: Ureteral electrothermal injury is visible via histology ex vivo (Photo courtesy of Long et al., doi 10.1117/1.BIOS.1.1.015001)

Minimally Invasive Imaging Technique to Revolutionize Ureteral Injury Detection

Electrothermal ureteral injuries are a frequent complication during pelvic surgery. The ureters, which are delicate tubes carrying urine from the kidneys to the bladder, are especially at risk due to their... 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.