A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity.

Ahmmed, Syed; Mondal, M Rubaiyat Hossain; Mia, Md Raihan; Adibuzzaman, Mohammad; Hoque, Abu Sayed Md Latiful; Ahamed, Sheikh Iqbal

Ahmmed, Syed; Mondal, M Rubaiyat Hossain; Mia, Md Raihan; Adibuzzaman, Mohammad; Hoque, Abu Sayed Md Latiful; Ahamed, Sheikh Iqbal.

Afiliación

Ahmmed S; Institute of Information and Communication Technology, Bangladesh University of Engineering And Technology, Dhaka, Bangladesh.
Mondal MRH; Institute of Information and Communication Technology, Bangladesh University of Engineering And Technology, Dhaka, Bangladesh.
Mia MR; Department of Computer Science And Engineering, Bangladesh University of Engineering And Technology, Dhaka, Bangladesh.
Adibuzzaman M; Department of Computer Science, Marquette University, Milwaukee, WI, USA.
Hoque ASML; Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR, USA.
Ahamed SI; Department of Computer Science And Engineering, Bangladesh University of Engineering And Technology, Dhaka, Bangladesh.

Heliyon ; 9(11): e21523, 2023 Nov.

Article en En | MEDLINE | ID: mdl-38034661

Palabras clave

Data quality; Data science; Electronic health records; LOINC; Machine learning; SNOMED CT; Standardization; String distance similarity

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Heliyon Año: 2023 Tipo del documento: Article País de afiliación: Bangladesh

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google