Distribution-Based Similarity Measures Applied to Laboratory Results Matching.

Courtois, M.; Filiot, A.; Ficheur, Gregoire

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.3233/SHTI210823

PMID :

34795089

URL permanente :

http://hdl.handle.net/20.500.12210/90205

Titre :

Distribution-Based Similarity Measures Applied to Laboratory Results Matching.

Auteur(s) :

Courtois, M. [Auteur]
Filiot, A. [Auteur]
Ficheur, Gregoire [Auteur]

METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Titre de la revue :

Studies in Health Technology and Informatics

Nom court de la revue :

Stud Health Technol Inform

Numéro :

287

Pagination :

94-98

Date de publication :

2021

ISSN :

1879-8365

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

The use of international laboratory terminologies inside hospital information systems is required to conduct data reuse analyses through inter-hospital databases. While most terminology matching techniques performing ...
Lire la suite >The use of international laboratory terminologies inside hospital information systems is required to conduct data reuse analyses through inter-hospital databases. While most terminology matching techniques performing semantic interoperability are language-based, another strategy is to use distribution matching that performs terms matching based on the statistical similarity. In this work, our objective is to design and assess a structured framework to perform distribution matching on concepts described by continuous variables. We propose a framework that combines distribution matching and machine learning techniques. Using a training sample consisting of correct and incorrect correspondences between different terminologies, a match probability score is built. For each term, best candidates are returned and sorted in decreasing order using the probability given by the model. Searching 101 terms from Lille University Hospital among the same list of concepts in MIMIC-III, the model returned the correct match in the top 5 candidates for 96 of them (95%). Using this open-source framework with a top-k suggestions system could make the expert validation of terminologies alignment easier.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CHU Lille

Collections :

METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2023-11-15T05:31:57Z
2024-01-11T12:30:50Z

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Ce document est diffusé sous licence Attribution-NonCommercial 3.0 United States

Numéro de version	Lien	Date de modification
2	20.500.12210/90205*	2024-01-11T12:11:41Z
1	20.500.12210/90205.1	2023-11-15T05:31:57Z

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