Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach.
BMC Med Inform Decis Mak
; 23(1): 20, 2023 01 26.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2214579
ABSTRACT
BACKGROUND:
Extracting relevant information about infectious diseases is an essential task. However, a significant obstacle in supporting public health research is the lack of methods for effectively mining large amounts of health data.OBJECTIVE:
This study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature.METHODS:
The proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports.RESULTS:
The named entity recognition implementation in the NLP layer achieves a performance gain of about 1-3% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1-8% better). A thorough examination reveals the disease's presence and symptoms prevalence in patients.CONCLUSIONS:
A similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Lenguaje Natural
/
COVID-19
Tipo de estudio:
Reporte de caso
/
Estudio de cohorte
/
Estudio experimental
/
Estudio observacional
/
Estudio pronóstico
Límite:
Humanos
Idioma:
Inglés
Revista:
BMC Med Inform Decis Mak
Asunto de la revista:
Informática Médica
Año:
2023
Tipo del documento:
Artículo
País de afiliación:
S12911-023-02117-3
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