Automatic phenotyping of electronical health record: PheVis algorithm - Archive ouverte HAL Access content directly
Journal Articles Journal of Biomedical Informatics Year : 2021

Automatic phenotyping of electronical health record: PheVis algorithm

(1) , (2) , (3) , (3) , (2) , (4, 2, 5)
1
2
3
4
5

Abstract

Electronic Health Records (EHRs) often lack reliable annotation of patient medical conditions. Yu et al . recently proposed PheNorm , an automated unsupervised algorithm to identify patient medical conditions from EHR data. PheVis extends PheNorm at the visit resolution. PheVis combines diagnosis codes together with medical concepts extracted from medical notes, incorporating past history in a machine learning approach to provide an interpretable “white box” predictor of the occurrence probability for a given medical condition at each visit. PheVis is applied to two real-world use-cases using the datawarehouse of the University Hospital of Bordeaux: i) rheumatoid arthritis, a chronic condition; ii) tuberculosis, an acute condition (cross-validated AUROC were respectively 0.948 [0.945 ; 0.950] and 0.987 [0.983 ; 0.990]). PheVis performs well for chronic conditions, though absence of exclusion of past medical history by natural language processing tools limits its performance in French for acute conditions.
Fichier principal
Vignette du fichier
PheVis_JBI_v8_clean.pdf (1.25 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03100435 , version 1 (07-01-2021)

Identifiers

Cite

Thomas Ferté, Sébastien Cossin, Thierry Schaeverbeke, Thomas Barnetche, Vianney Jouhet, et al.. Automatic phenotyping of electronical health record: PheVis algorithm. Journal of Biomedical Informatics, 2021, 117, pp.103746. ⟨10.1016/j.jbi.2021.103746⟩. ⟨hal-03100435⟩
99 View
299 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More