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CrowdMapping: A Crowdsourcing-Based Terminology Mapping Method for Medical Data Standardization.

Huajian Mao¹, Chenyang Chi¹, Boyu Huang¹

¹Institute of Health Service and Medical Informatics, Academy of Military Medical Sciences of Chinese PLA, Beijing, China.

Studies in Health Technology and Informatics

|January 4, 2018

Summary

This summary is machine-generated.

This study introduces CrowdMapping, a crowdsourcing method for standardizing medical terminology across different healthcare systems. It enhances data exchange and analysis by creating more sophisticated mapping rules through diverse inputs and user interaction.

Keywords:

Controlled Crowdsourcing Medical Informatics Vocabulary

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Area of Science:

Medical Informatics
Health Data Science

Background:

Electronic health record (EHR) systems utilize unique terminology, hindering data exchange.
Standardized medical terminology is crucial for effective clinical data analysis and interoperability.

Purpose of the Study:

To propose CrowdMapping, a crowdsourcing-based method for standardizing medical terminology.
To improve the accuracy and sophistication of terminology mapping for medical data analysis.

Main Methods:

Developed a crowdsourcing-based terminology mapping method named CrowdMapping.
Utilized a confidential model incorporating mappings from multiple healthcare organizations.
Evaluated mapping diversity to refine mapping rules and enabled user interaction for model evaluation.

Main Results:

Presented initial results of the CrowdMapping system for terminology mapping.
Demonstrated the potential of crowdsourcing and diverse mapping inputs for standardization.

Conclusions:

CrowdMapping offers a novel approach to medical terminology standardization.
The method has the potential to enhance data exchange and analysis in clinical processes.