Accuracy of using natural language processing methods for identifying healthcare-associated infections

There is a growing interest in using natural language processing (NLP) for healthcare-associated infections (HAIs) monitoring. A French project consortium, SYNODOS, developed a NLP solution for detecting medical events in electronic medical records for epidemiological purposes. The objective of this study was to evaluate the performance of the SYNODOS data processing chain for detecting HAIs in clinical documents.

Mots clés

Medical records systems Decision support systems Clinical Healthcare-associated infections computerized Natural language processing Epidemiology

Domaines

Informatique [cs]

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https://hal.science/hal-02109408

Soumis le : mercredi 24 avril 2019-21:28:17

Dernière modification le : jeudi 31 octobre 2024-03:21:51

Dates et versions

hal-02109408 , version 1 (24-04-2019)

Identifiants

HAL Id : hal-02109408 , version 1
DOI : 10.1016/j.ijmedinf.2018.06.002
PUBMED : 30032970

Citer

Nastassia Tvardik, Ivan Kergourlay, Frederique Segond, Stefan J. Darmoni, Marie-Hélène Metzger. Accuracy of using natural language processing methods for identifying healthcare-associated infections. International Journal of Medical Informatics, 2018, 117, pp.96-102. ⟨10.1016/j.ijmedinf.2018.06.002⟩. ⟨hal-02109408⟩

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