Parisa Rashidi

Title: Parisa Rashidi: Innovator in Patient Prediction Technologies

Introduction

Parisa Rashidi is a prominent inventor based in Gainesville, FL (US). She has made significant contributions to the field of healthcare technology, particularly in the area of patient prediction systems. With a total of 4 patents, her work focuses on utilizing advanced machine learning techniques to improve patient outcomes.

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 generate predictions for patients based on their medical data. The system is designed to update predictions in real-time, allowing clinicians to make informed decisions based on the most current information.

Another notable patent is "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 analyzing existing clinical data in electronic health records, the system generates personalized risk panels that help healthcare providers anticipate and manage potential complications.

Career Highlights

Parisa Rashidi 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, Parisa has collaborated with esteemed colleagues, including Azra Bihorac and Tyler J Loftus. These partnerships have further enhanced her research and development efforts in the healthcare technology sector.

Conclusion

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