Diagnostic Performance Evaluation of Deep Learning-Based Medical Text Modelling to Predict Pulmonary Diseases from Unstructured Radiology Free-Text Reports

Abstract

The third most common cause of death worldwide is attributed to pulmonary diseases, making it imperative to diagnose them promptly. Radiology is a medical discipline that utilizes medical imaging to guide treatment. Radiologists prepare reports interpreting details and findings analysed from medical images. Radiology free-text reports are a rich source of textual information that can be exploited to enhance the efficacy of medical prognosis, treatment and research. Radiology reports exist in an unstructured format as are not suitable by themselves for mathematical computation or machine learning operations. Therefore, natural language processing (NLP) strategies are employed to convert unstructured natural language text into a structured format that can be fed into machine learning (ML) or deep learning (DL) models for information extraction. We propose a DL-based medical text modelling framework incorporating a knowledge base to predict pulmonary diseases from unstructured radiology free-text reports. We make detailed diagnostic performance evaluations of our proposed technique by comparing it with state-of-the-art NLP techniques on radiology free-text reports extracted from two medical institutions. The comprehensive analysis shows that the proposed model achieves superior results compared to existing state-of-the-art text modelling techniques.

Keywords

• radiology reports
• unstructured data
• natural language processing
• deep learning