University of California, Irvine-Pathology Extraction Pipeline: the pathology extraction pipeline for information extraction from pathology reports

We describe Pathology Extraction Pipeline (PEP)--a new Open Health Natural Language Processing pipeline that we have developed for information extraction from pathology reports, with the goal of populating the extracted data into a research data warehouse. Specifically, we have built upon Medical Kn...

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Bibliographic Details
Published in:Health informatics journal Vol. 20; no. 4; p. 288
Main Authors: Ashish, Naveen, Dahm, Lisa, Boicey, Charles
Format: Journal Article
Language:English
Published: England 01.12.2014
Subjects:
Academic Medical Centers
California
Data Mining - methods
Decision Support Systems, Clinical
Electronic Health Records - utilization
Evidence-Based Practice
Female
Hospitals, University
Humans
Information Storage and Retrieval - methods
Male
Natural Language Processing
Neoplasms - pathology
Pathology, Clinical - methods
Systems Integration
California
evidence-based practice
decision-support systems
databases and data mining
information and knowledge management
Clinical decision-making
ISSN:1741-2811, 1741-2811
Online Access:Get more information
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Description
Summary:We describe Pathology Extraction Pipeline (PEP)--a new Open Health Natural Language Processing pipeline that we have developed for information extraction from pathology reports, with the goal of populating the extracted data into a research data warehouse. Specifically, we have built upon Medical Knowledge Analysis Tool pipeline (MedKATp), which is an extraction framework focused on pathology reports. Our particular contributions include additional customization and development on MedKATp to extract data elements and relationships from cancer pathology reports in richer detail than at present, an abstraction layer that provides significantly easier configuration of MedKATp for extraction tasks, and a machine-learning-based approach that makes the extraction more resilient to deviations from the common reporting format in a pathology reports corpus. We present experimental results demonstrating the effectiveness of our pipeline for information extraction in a real-world task, demonstrating performance improvement due to our approach for increasing extractor resilience to format deviation, and finally demonstrating the scalability of the pipeline across pathology reports for different cancer types.
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ISSN:1741-2811
1741-2811
DOI:10.1177/1460458213494032