Meeting Banner
Abstract #0686

Designing a Clinical Decision Support System for MRI Radiology Titles Using Machine Learning Techniques and Electronic Medical Records

Peyman Shokrollahi1, Juan M. Zambrano Chaves1, Jonathan P.H. Lam1, Avishkar Sharma1, Debashish Pal2, Naeim Bahrami2, Akshay S. Chaudhari1, and Andreas M. Loening1
1Radiology, Stanford University, Stanford, CA, United States, 2GE Healthcare, Sunnyvale, CA, United States


The use of inappropriate radiology protocols introduces risk of missed and incomplete diagnoses, thus endangering patient health, potentially prolonging treatment, and increasing healthcare costs. A clinical decision support system based on machine learning and electronic medical records of patients undergoing MRI was developed to predict radiology titles and their probabilities for radiologist review. A cumulative F1-score of ~85% was obtained for the top three predicted titles. The proposed system can guide physicians toward selecting appropriate titles and alert radiologists of potentially inappropriate selections, thereby improving imaging utility and increasing diagnostic accuracy, which favors better patient outcomes.

This abstract and the presentation materials are available to members only; a login is required.

Join Here