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Using Optimal Feature Selection and Continuous Learning to Implement Efficient Model Arrays for Predicting Daily

Leslie K Lee¹, Melissa Viator², Catherine S Giess¹

¹Mass General Brigham Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.

Academic Radiology

|March 27, 2025

Summary

This summary is machine-generated.

A machine learning model accurately forecasts daily radiology workload using key metrics like unread exams and future schedules. This AI tool helps manage clinical practice by predicting image interpretation volume.

Keywords:

Continuous learning Optimal feature subset Workflow prediction

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Area of Science:

Radiology
Machine Learning
Healthcare Management

Background:

Daily clinical workload in radiology practices is subject to significant fluctuations.
Accurate forecasting of daily image interpretation volume is crucial for efficient practice management.

Purpose of the Study:

To design, validate, and implement a machine learning model for predicting daily clinical radiology workload.
To develop an efficient and sustainable AI solution for workload forecasting.

Main Methods:

Analysis of one year of radiology exam volume data from two academic medical centers.
Utilized optimal feature selection and various machine learning models to identify the most accurate prediction method.
Implemented continuous learning for sustained model performance and weekly retraining with live data.

Main Results:

A continuously learning linear regression model array, using three key features, achieved an average R² of 0.83.
The model accurately predicted daily clinical workload, outperforming simpler estimation methods.
The AI solution was successfully deployed into an online dashboard for visualization.

Conclusions:

An artificial intelligence (AI) model can be effectively developed and implemented to forecast daily clinical radiology workload.
This AI-driven forecasting serves as a valuable practice management tool for radiology departments.