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Case Studies

Case Studies

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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

Domain-specific analytical language modeling--the chief complaint as a case study.

Jari Yli-Hietanen¹, Samuli Niiranen, Michael Aswell

¹Department of Signal Processing, Tampere University of Technology, Tampere, Finland. jari.yli-hietanen@tut.fi

International Journal of Medical Informatics

|March 25, 2009

Summary

This study enhances automation in electronic medical records by computationally understanding emergency department chief complaints. An accurate classification algorithm, using approximate matching, improves both correctness and completeness of medical text analysis.

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

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Area of Science:

Computational linguistics
Medical informatics
Natural language processing

Background:

Electronic medical records (EMRs) contain significant free-text data, hindering automation.
Developing structured data standards for diverse clinical information is challenging.
Automating the analysis of unstructured medical text is crucial for EMR advancement.

Purpose of the Study:

To improve automation in EMRs by computationally understanding emergency department chief complaints.
To develop and evaluate a domain-specific analytical model for chief complaint classification.
To enable applications like automatic syndromic surveillance using classified chief complaints.

Main Methods:

Applied domain-specific analytical modeling for computational understanding of chief complaints.
Developed an algorithm for automatic classification of emergency department chief complaints.
Incorporated approximate matching to handle typographic variations in text.

Main Results:

The algorithm achieved accurate classification correctness in a multi-hospital setting.
Approximate matching did not negatively impact classification correctness.
Approximate matching significantly increased classification completeness for chief complaints.

Conclusions:

Computational understanding of emergency department chief complaints is feasible and effective.
The developed algorithm enhances EMR automation through accurate and complete text classification.
This approach supports advanced applications like automated public health surveillance.