Analysis of Language Embeddings for Classification of Unstructured Pathology Reports

Area of Science:

• Digital pathology
• Computational pathology
• Natural Language Processing (NLP)

Background:

• Pathology reports are crucial medical documents offering insights into biopsy samples.
• Digital pathology integrates high-resolution images with these reports.
• Currently, pathology reports are underutilized in computational pathology advancements.

Purpose of the Study:

• To analyze unstructured pathology reports using NLP techniques.
• To compare the effectiveness of various word embedding models for pathology report analysis.
• To explore the potential of NLP in enhancing computational pathology.

Main Methods:

• Comparative analysis of embedding models: BioBERT, Clinical BioBERT, BioMed-RoBERTa.
• Evaluation of Term Frequency-Inverse Document Frequency (TF-IDF).
• Assessment of combined pre-trained embeddings with TF-IDF.

Main Results:

• Word embedding techniques demonstrate significant effectiveness in analyzing pathology reports.
• Specific models like BioBERT and its variants show promise for extracting information.
• TF-IDF and combined approaches also yield valuable results.

Conclusions:

• Word embedding models are effective tools for analyzing complex medical terminology in pathology reports.
• This analysis highlights a pathway for integrating NLP into computational pathology.
• Further research can leverage these findings to unlock the full potential of pathology reports.