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Updated: Jul 17, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Disseminating natural language processed clinical narratives.

Elizabeth S Chen¹, George Hripcsak, Carol Friedman

¹Department of Biomedical Informatics, Columbia University, New York, NY, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|January 24, 2007

Summary

This study introduces a simplified tabular format for Natural Language Processing (NLP) clinical data. This format effectively identifies clinical conditions, making NLP data more accessible to various users.

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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

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Published on: February 16, 2011

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Area of Science:

Clinical Informatics
Natural Language Processing
Data Representation

Background:

Clinical narratives contain valuable patient information.
Current Natural Language Processing (NLP) output formats (e.g., XML) can be complex for end-users.
A simplified format is needed for broader accessibility and use in tools like spreadsheets and databases.

Purpose of the Study:

To design a knowledge-based tabular representation for NLP output.
To develop a transformation program for the MedLEE NLP system's output.
To evaluate the effectiveness of the simplified tabular format.

Main Methods:

Knowledge-based design of a tabular data structure.
Development of a transformation program for MedLEE NLP output.
Evaluation comparing the tabular format with existing complex NLP formats.

Main Results:

A simplified tabular representation for NLP output was designed and implemented.
A transformation program for MedLEE was successfully developed.
The tabular format demonstrated comparable effectiveness to complex formats in identifying clinical conditions.

Conclusions:

A simplified tabular format can effectively represent clinical NLP data.
This format enhances the accessibility and usability of clinical NLP information.
The approach facilitates data dissemination for users familiar with spreadsheet and database tools.