Indexat a

Llicència i ús

Citacions

Altmetrics

Anàlisi d'autories institucional

Muñoz Monjas, AAutor (correspondència)

Compartir

Automatic Outlier Detection in Laboratory Result Distributions Within a Real World Data Network

Publicat a:Caring Is Sharing-Exploiting The Value In Data For Health And Innovation-Proceedings Of Mie 2023. 302 88-92 - 2023-01-01 302(), DOI: 10.3233/SHTI230070.

Autors: Muñoz Monjas, A; Rubio Ruiz, D; Pérez-Rey, D; Palchuk, M

Afiliacions

Resum

Laboratory data must be interoperable to be able to accurately compare the results of a lab test between healthcare organizations. To achieve this, terminologies like LOINC (Logical Observation Identifiers, Names and Codes) provide unique identification codes for laboratory tests. Once standardized, the numeric results of laboratory tests can be aggregated and represented in histograms. Due to the characteristics of Real World Data (RWD), outliers and abnormal values are common, but these cases should be treated as exceptions, excluding them from possible analysis. The proposed work analyses two methods capable of automating the selection of histogram limits to sanitize the generated lab test result distributions, Tukey's box-plot method and a Distance to Density approach, within the TriNetX Real World Data Network. The generated limits using clinical RWD are generally wider for Tukey's method and narrower for the second method, both greatly dependent on the values used for the algorithm's parameters.

Paraules clau