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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
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Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology
M Sevenster1, J Buurman2, P Liu3
1Philips Research, Briarcliff Manor , NY, United States.
Applied Clinical Informatics
|October 9, 2015
Summary
This study developed natural language processing (NLP) engines to extract and categorize measurements from radiology reports. These tools improve the machine understanding of clinical data for better healthcare outcomes.
Area of Science:
- Medical Informatics
- Natural Language Processing
- Radiology
Background:
- Quantitative outcome parameters are crucial for reproducible healthcare.
- Measurements in imaging studies are key quantitative parameters.
Purpose of the Study:
- Develop and evaluate NLP engines for extracting measurements from radiology reports.
- Categorize extracted measurements by temporality.
Main Methods:
- Measurement extraction using regular expressions, evaluated against ground truth.
- Temporality categorization as a machine learning problem using a maximum entropy model.
- Model evaluated via ten-fold cross-validation.
Main Results:
- Measurement extraction engine achieved 0.994 precision and 0.991 recall.
- Measurement classification engine demonstrated 0.960 accuracy.
Conclusions:
- Contributes to machine understanding of radiology reports.
- Potential applications in medical data processing software.
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