• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • CONTACT US
Wednesday, February 8, 2023
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • CONTACT US
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • CONTACT US
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News

AI system to predict patients at higher risk for diabetes complications

Bioengineer by Bioengineer
November 3, 2022
in Science News
Reading Time: 3 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

More than 37 million people in the United States have diabetes but many don’t receive timely care which can lead to costly, even deadly complications. While effective treatments are available in primary care settings, clinicians lack the tools necessary to identify those at the highest risk. To prevent poor health outcomes before they occur, researchers at the University of Houston are developing Primary Care Forecast, a clinical decision support system that uses deep learning to predict which patients are more likely to experience complications.

Dr. Winston Liaw

Credit: University of Houston

More than 37 million people in the United States have diabetes but many don’t receive timely care which can lead to costly, even deadly complications. While effective treatments are available in primary care settings, clinicians lack the tools necessary to identify those at the highest risk. To prevent poor health outcomes before they occur, researchers at the University of Houston are developing Primary Care Forecast, a clinical decision support system that uses deep learning to predict which patients are more likely to experience complications.

The first tool to be developed within the innovative AI system is the Diabetes Complication Severity Index (DCSI) Progression Tool, which, in addition to a patient’s health history, considers how their social and environmental circumstances – employment status, living arrangement, education level, food security – could increase their risk for complications. Research shows these societal factors can affect disease progression.

Funded by the American Board of Family Medicine, the tool will provide clinicians with timely, actionable insights so they can intervene early, reduce the percentage of individuals with diabetes who have complications, and lower the number of complications affecting each patient.

“Our long-term goal is to help clinicians become more proactive and less reactive when treating diabetes. By leveraging the capabilities of artificial intelligence and machine learning, we can more effectively connect at-risk individuals with interventions before they become sicker,” said Dr. Winston Liaw, the principal investigator of the project and chair of the Department of Health Systems and Population Health Sciences at the Tilman J. Fertitta Family College of Medicine. 

For years, insurance companies and researchers alike have used the DCSI to quantify patients’ complications at a single point in time. Still, no tools exist to predict which individuals are at the most significant risk for rising DCSI scores.

The tool will be developed in collaboration with the Humana Integrated Health System Sciences Institute at the University of Houston, and leverage unique data sets from Humana Inc. – claims, health records, and individual and community social risk factors. The tool will be tested within the PRIME Registry, a national platform that includes millions of primary care patients nationwide.

“The challenge with existing prediction tools is they provide little explanation and no guidance for subsequent action, limiting trust and implementation. The tool we are developing will inform clinicians why patients are at risk and suggest actions to reduce that risk,” said Ioannis Kakadiaris, Hugh Roy and Lillie Cranz Cullen University Professor of Computer Science and Health Systems and Population Health Sciences.

“Humana is excited to collaborate with our partners at the University of Houston leveraging their AI and predictive analytic expertise with our extensive diabetes experience using the DCSI and health impactful social determinant solutions. This tool represents a great opportunity to put actionable information into the hands of primary care physicians at the point of service where real change in health happens,” said Dr. Todd Prewitt, corporate medical director, clinical strategy and analytics at Humana.

Beyond diabetes, the researchers believe the tool could help predict complications associated with other conditions, such as uncontrolled hypertension or worsening depression. The tool will be especially relevant as the health care industry shifts to a value-based care model where doctors are rewarded for improving patients’ health instead of being paid for each visit, procedure, or test, regardless of the outcome.

The Fertitta Family College of Medicine, founded in 2019 on a social mission to improve health and health care in underserved urban and rural communities across Texas, emphasizes primary care education and research.

“As primary care doctors, we need an efficient way to leverage the massive amounts of information we receive to improve the quality of life of our patients. The number of complications a patient experiences is strongly associated with death or hospitalization, so developing this AI tool is critical,” said Liaw.



Share12Tweet8Share2ShareShareShare2

Related Posts

A schematic of the beam diameter measurement using transmitted X-rays old and new methods

Size of X-Ray beams successfully evaluated with mathematics

February 8, 2023
Flight Bones

Scientists develop new index based on functional morphology to understand how ancestors of modern birds used their wings

February 8, 2023

Immunaeon joins the RegenMed Hub

February 8, 2023

Novel method to design new peptide therapeutics pioneered

February 8, 2023

POPULAR NEWS

  • Jean du Terrail, Senior Machine Learning Scientist at Owkin

    Nature Medicine publishes breakthrough Owkin research on the first ever use of federated learning to train deep learning models on multiple hospitals’ histopathology data

    66 shares
    Share 26 Tweet 17
  • Metal-free batteries raise hope for more sustainable and economical grids

    41 shares
    Share 16 Tweet 10
  • One-pot reaction creates versatile building block for bioactive molecules

    37 shares
    Share 15 Tweet 9
  • Duke-NUS and NHCS scientists first in the world to regenerate diseased kidney

    37 shares
    Share 15 Tweet 9

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Size of X-Ray beams successfully evaluated with mathematics

Scientists develop new index based on functional morphology to understand how ancestors of modern birds used their wings

Immunaeon joins the RegenMed Hub

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 43 other subscribers
  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In