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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

Towards temporal relation discovery from the clinical narrative.

Guergana Savova¹, Steven Bethard, Will Styler

¹Mayo Clinic, Rochester, MN, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|March 31, 2010

Summary

This study explores automated temporal relation discovery in clinical text. Findings pave the way for advanced clinical text analysis and understanding disease progression.

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

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Published on: September 20, 2018

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

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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

Area of Science:

Clinical informatics
Natural Language Processing (NLP)
Computational linguistics

Background:

Understanding disease progression and patient history requires temporal context from clinical narratives.
Automated extraction of temporal relations and timelines from clinical text is crucial for large-scale data mining.
Current methods in general NLP need evaluation for their applicability in the specialized clinical domain.

Purpose of the Study:

To evaluate the portability of deep parsing and semantic role labeling for temporal relation discovery in clinical text.
To develop a domain-specific annotation schema for temporal relations based on TimeML standards.
To establish foundational steps for building a comprehensive system for automated temporal relation discovery in healthcare.

Main Methods:

Evaluation of general NLP techniques: deep parsing and semantic role labeling.
Development of a temporal relation annotation schema aligned with TimeML.
Initial testing and analysis of method performance within the clinical domain.

Main Results:

Demonstrated the feasibility of applying general NLP methods to clinical text for temporal analysis.
Established a foundational annotation schema for temporal relations in a clinical context.
Identified areas for domain-specific refinement of NLP technologies.

Conclusions:

The initial steps show promise for automated temporal relation discovery in clinical narratives.
Domain-specific adaptation of NLP tools is necessary for robust clinical text analysis.
This work lays the groundwork for enhancing Mayo's clinical Text Analysis and Knowledge Extraction System.