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Related Concept Videos

Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

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A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains...
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Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
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Related Experiment Video

Updated: Aug 15, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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An accessible, efficient, and accurate natural language processing method for extracting diagnostic data from

Hansen Lam1, Freddy Nguyen1, Xintong Wang1

  • 1Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Journal of Pathology Informatics
|January 6, 2023
PubMed
Summary

This study developed a natural language processing (NLP) algorithm to automate pathology report data extraction. The NLP method proved highly accurate and significantly faster than manual extraction for research purposes.

Keywords:
AlgorithmCarcinomaExtractionFree-textNarrativePythonUnstructuredXML

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Area of Science:

  • Computational pathology
  • Medical informatics
  • Natural Language Processing

Background:

  • Manual data extraction from pathology reports is time-consuming and error-prone.
  • Clinical and translational research requires efficient access to pathology data for quality assessment.

Purpose of the Study:

  • To develop and evaluate a dictionary- and rule-based natural language processing (NLP) algorithm.
  • To automate the extraction of searchable data from surgical pathology reports.
  • To assess the accuracy and efficiency of the automated method compared to manual extraction.

Main Methods:

  • Pathology data were exported from the laboratory information system (LIS) into XML documents.
  • A Python-based NLP algorithm parsed XML data into desired data points.
  • Extracted data were delivered to Excel spreadsheets for analysis.
  • Concordance with manual extraction was measured using Cohen's κ coefficient and P values.

Main Results:

  • The automated NLP method demonstrated high concordance (90%-100%, P<.001) with manual extraction.
  • Excellent inter-observer reliability (Cohen's κ: 0.86-1.0) was achieved.
  • The automated method was 24-39 times faster than manual data extraction.
  • The system linked extracted diagnoses to additional variables like patient age and location.

Conclusions:

  • A simple, flexible, and scalable NLP platform can accurately and efficiently extract linked data from pathology reports.
  • This automated approach facilitates clinical and research purposes by providing searchable spreadsheets.
  • The NLP-based method offers a correct, safe, and quick solution for pathology data management.