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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

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Extracting templates from radiology reports using sequence alignment.

Shengyang Wu¹, Curtis P Langlotz, Paras Lakhani

¹Department of Radiology, University of Michigan, Ann Arbor, MI 48104, USA. speterwu@med.umich.edu

International Journal of Data Mining and Bioinformatics

|January 30, 2013

Summary

This summary is machine-generated.

Radiologists can save time and improve consistency by using a new method called Radiology Content Alignment (RADICAL). This approach efficiently extracts common report templates, streamlining the dictation process for medical documentation.

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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:

Medical Informatics
Natural Language Processing
Radiology

Background:

Healthcare providers frequently dictate reports using template slots.
Radiologists often create personalized templates, leading to time inefficiency and physician-to-physician inconsistencies.
Standardized template extraction is needed to improve reporting efficiency.

Purpose of the Study:

To introduce Radiology Content Alignment (RADICAL), a novel method for template extraction from radiology reports.
To demonstrate the efficiency of RADICAL in identifying common report structures.
To provide examples of extracted templates and their slot contents.

Main Methods:

Utilized a sequence alignment approach based on dynamic programming.
Applied the RADICAL method to a corpus of radiology reports.
Analyzed the extracted templates and the data within their slots.

Main Results:

Successfully extracted common templates from a set of radiology reports.
Demonstrated the efficiency of the RADICAL method in identifying shared reporting structures.
Provided concrete examples of extracted templates and their corresponding slot fillers.

Conclusions:

RADICAL offers an efficient solution for extracting common templates from dictated reports.
The method has the potential to reduce time spent on template creation and enhance consistency in medical documentation.
Automated template extraction can improve the overall efficiency of radiology reporting.