Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2009 Jul-Aug;27(4):215-23; quiz 224-5.
doi: 10.1097/NCN.0b013e3181a91b58.

Exploring the ability of natural language processing to extract data from nursing narratives

Sookyung Hyun  1 , Stephen B Johnson, Suzanne Bakken
Affiliations

Exploring the ability of natural language processing to extract data from nursing narratives

Sookyung Hyun et al. Comput Inform Nurs. 2009 Jul-Aug.

Abstract

Natural Language Processing (NLP) offers an approach for capturing data from narratives and creating structured reports for further computer processing. We explored the ability of a NLP system, Medical Language Extraction and Encoding (MedLEE), on nursing narratives. MedLEE extracted 490 concepts from narrative text in a sample of 553 oncology nursing process notes. The most frequently monitored and recorded signs and symptoms were related to chemotherapy care, such as adverse reactions, shortness of breath, nausea, pain, and bleeding. In terms of nursing interventions, chemotherapy, blood culture, medication, and blood transfusion were commonly recorded in free text. NLP may provide a feasible approach to extract data related to patient safety/quality measures and nursing outcomes by capturing nursing concepts that are not recorded through structured data entry. For better NLP performance in the domain of nursing, additional nursing terms and abbreviations must be added to MedLEE's lexicon.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Procedure
Figure 2
Figure 2
Procedure – Use for quality/safety
See this image and copyright information in PMC

References

    1. Barrows RC, Busuioc M, Friedman C. Limited parsing of notational text visit notes: Ad-hoc vs. NLP approaches. Proc AMIA Symp. 2000:51–5. - PMC - PubMed
    1. Hyun S, Bakken S, Friedman C, Johnson S. Natural language processing challenges in HIV/AIDS clinic notes. AMIA Annu Symp Proc. 2003:872. - PMC - PubMed
    1. Jha A, Kuperman G, Teich J, Leape L, Shea B, Rittenberg E, et al. Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J Am Med Inform Assoc. 1998;5(3):305–14. - PMC - PubMed
    1. Friedman C. A broad coverage Natural Language processing system. Proc AMIA Symp. 2000:270–4. - PMC - PubMed
    1. Honigman BA. A computerized method for identifying incidents associated with adverse drug events in outpatients. International Journal of Medical Informatics. 2001;61(1):21–32. - PubMed

Publication types