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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Published on: September 20, 2018

Natural language processing in pathology: a scoping review.

Gerard Burger¹, Ameen Abu-Hanna², Nicolette de Keizer²

¹Symbiant Pathology Expert Centre, Hoorn, The Netherlands Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.

Journal of Clinical Pathology

|July 25, 2016

Summary

This summary is machine-generated.

Natural language processing (NLP) effectively encodes free-text pathology data for registries. While various NLP methods show high performance, a lack of standardized validation and datasets hinders direct comparison of their merits.

Keywords:

COMPUTER SYSTEMS REPORTS SURGICAL PATHOLOGY

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Area of Science:

Computational pathology
Medical informatics
Natural language processing

Background:

Pathology data is crucial for medical registries and analysis.
Pathology information is frequently unstructured free text.

Approach:

A systematic review of 38 papers on NLP in pathology was conducted.
Searches were performed in PubMed, ACM Digital Library, and ACL Anthology.
Included studies were analyzed for objectives, NLP methods, validation, software, performance, and practical application.

Key Points:

The primary goals were encoding and extracting clinical information from pathology reports.
Common NLP methods include word/phrase matching, machine learning, and rule-based systems.
18 studies reported performance metrics (F-measure, recall, precision) exceeding 0.9; proprietary software and GATE were frequently used.

Conclusions:

Multiple NLP methods demonstrate strong performance in pathology data encoding.
The lack of standardized validation and shared datasets impedes comparative analysis.
Further research is needed for comparative analysis and validation to understand method performance.