Data for registry and quality review can be retrospectively collected using natural language processing from unstructured charts of arthroplasty patients.

Journal: The bone & joint journal

Published Date: Jul 1, 2020

Abstract

AIMS: Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction.

Authors

Romil F Shah

Department of Orthopedic Surgery, University of California-San Francisco, San Francisco, CA.
Stefano Bini

Department of Orthopedic Surgery, University of California - San Francisco, San Francisco, California, USA.
Thomas Vail

Department of Orthopedic Surgery, University of California - San Francisco, San Francisco, California, USA.

Keywords

Algorithms Arthroplasty, Replacement, Hip Arthroplasty, Replacement, Knee Data Accuracy Humans Information Storage and Retrieval Natural Language Processing Quality of Health Care Registries Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (32600201)

Data for registry and quality review can be retrospectively collected using natural language processing from unstructured charts of arthroplasty patients.

Abstract

Authors

Keywords

External Resources

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