Neuradicon: Operational representation learning of neuroimaging reports.

Area of Science:

• Medical Informatics
• Natural Language Processing
• Radiology

Background:

• Radiological reports are unstructured, limiting quantitative analysis and operational optimization.
• Current monitoring of radiology services lacks content-specific differentiation.
• Need for advanced tools to analyze and leverage information within radiology reports.

Purpose of the Study:

• Introduce Neuradicon, a novel NLP framework for quantitative analysis of neuroradiological reports.
• Develop a system for operational guidance using structured data from unstructured reports.
• Enable targeted operational optimization within radiology departments.

Main Methods:

• Developed a hybrid framework combining rule-based and machine learning models.
• Utilized probabilistic models for text classification and tagging.
• Employed auto-encoders for latent representation learning and statistical mapping.

Main Results:

• Applied Neuradicon to 336,569 neuroradiological reports for operational phenotyping.
• Achieved excellent generalizability across different time periods and institutions.
• Reported high f1-scores (0.96) for pathology classification on prospective data.

Conclusions:

• Neuradicon effectively segments, analyzes, classifies, and interrogates neuroradiological reports.
• The framework extracts rich, quantitative, and actionable signals from unstructured text.
• Provides a blueprint for leveraging NLP in operational contexts within healthcare.