A Web Based-Bayesian Disease Recommender System

This technology is a disease recommendation system that assesses a given set of diagnoses that a patient has and identifies possible co-morbidities. The system can be used by doctors to discover additional diseases in their patients or by hospitals to …

This technology is a disease recommendation system that assesses a given set of diagnoses that a patient has and identifies possible co-morbidities. The system can be used by doctors to discover additional diseases in their patients or by hospitals to discover codes that may have been missed during the billing process.

Background:
There is a significant need for new software and systems to help improve healthcare. As the global population ages and the incidence of chronic disease rises, there is an increasing need to find ways to efficiently provide healthcare and diagnostic services in the most accurate way possible.

Patients frequently experience more than one disease at the same time, which can make diagnosis difficult, sometimes resulting in some of the conditions being missed by the healthcare provider. Also, secondary diseases may be in the process of developing in a patient but not yet manifested. This technology addresses these issues by predicting any additional disease given a set of primary diagnoses, and empowers the healthcare provider by including an accompanying probability.

Applications:

  • Healthcare
  • Diagnostic decision support
  • Identification of missed diagnostic codes during the billing process


Advantages:

  • Reduces risk of undiagnosed conditions
  • Predicts potential co-morbidity

Website

https://arizona.technologypublisher.com/tech/A_Web_Based-Bayesian_Disease_Recommender_System

Contact Information

TTO Home Page: https://arizona.technologypublisher.com

Name: Lewis Humphreys

Title: Licensing Manager, Eller College of Mngmt & OTT

Email: lewish@tla.arizona.edu