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Detecting modeling inconsistencies in SNOMED CT using a machine learning technique.

Ankur Agrawal¹, Kashifuddin Qazi¹

¹Department of Computer Science, Manhattan College, NY, USA.

Methods (San Diego, Calif.)

|May 23, 2020

Summary

This summary is machine-generated.

This study introduces a machine learning approach to automatically detect errors in SNOMED CT, a crucial clinical terminology. The method effectively identifies inconsistencies, improving the quality of electronic health records.

Keywords:

Contextual Auditing Lexical Analysis Machine Learning Quality Assurance SNOMED CT

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Area of Science:

Medical Informatics
Clinical Terminology
Machine Learning

Background:

SNOMED CT is a vital clinical reference terminology for interoperability in Electronic Health Records.
Ensuring the quality of SNOMED CT is essential due to its widespread healthcare adoption.
Manual auditing of SNOMED CT concepts is resource-intensive and identifying context-specific inconsistencies is challenging.

Purpose of the Study:

To develop and evaluate a context-based, machine learning technique for identifying potential quality issues in SNOMED CT.
To automate the detection of modeling inconsistencies within SNOMED CT concepts.
To improve the efficiency and effectiveness of SNOMED CT quality assurance processes.

Main Methods:

A context-based machine learning algorithm was developed to analyze SNOMED CT concepts.
The Clinical Finding and Procedure hierarchies were utilized as a testbed for the proposed method.
The algorithm was evaluated based on its ability to identify concept pairs with inconsistencies.

Main Results:

The proposed machine learning method successfully identified inconsistencies in 72% of concept pairs flagged by the algorithm.
The technique demonstrated effectiveness in both maximizing the identification of errors and providing context for these inconsistencies.
The study validated the efficacy of the algorithmic approach in quality assurance for SNOMED CT.

Conclusions:

The developed machine learning technique offers a scalable and efficient solution for SNOMED CT quality assurance.
This automated approach can significantly aid SNOMED International in reducing inconsistencies within the terminology.
Implementing such methods can enhance the reliability and utility of SNOMED CT in clinical practice and research.