Azra Bihorac

Title: Azra Bihorac: Innovator in Patient Prediction Technologies

Introduction

Azra Bihorac is a prominent inventor based in Gainesville, FL, known for her significant contributions to the field of patient prediction technologies. With a total of 3 patents, her work focuses on utilizing advanced machine learning techniques to improve patient outcomes in critical care settings.

Latest Patents

One of her latest patents is titled "Systems and methods for using deep learning to generate acuity scores for critically ill or injured patients." This invention provides methods, apparatus, systems, and computer program products that enable the generation of predictions for patients based on deep learning models. The model is trained to produce acuity scores or mortality predictions, which can be updated with real-time medical data to enhance patient care.

Another notable patent is the "Method and apparatus for prediction of complications after surgery." This invention employs an automated analytics framework to forecast patient-level probabilistic risk scores for major postoperative complications. By accessing and normalizing health record data, the system calculates risk probabilities and generates personalized risk panels, thereby aiding clinicians in making informed decisions.

Career Highlights

Azra Bihorac is affiliated with the University of Florida Research Foundation, Incorporated, where she continues to advance her research in patient prediction technologies. Her innovative work has garnered attention in the medical community, contributing to improved patient care and outcomes.

Collaborations

Throughout her career, Azra has collaborated with notable colleagues, including Parisa Rashidi and Tyler J Loftus. These partnerships have fostered a collaborative environment that enhances the development of cutting-edge technologies in healthcare.

Conclusion

Azra Bihorac's contributions to patient prediction technologies exemplify the impact of innovation in healthcare. Her patents reflect a commitment to improving patient outcomes through advanced analytics and machine learning.