New Search

Export article

Automatic phenotyping of electronical health record: PheVis algorithm

Thomas Ferté, , Thierry Schaeverbeke, Thomas Barnetche, Vianney Jouhet, Boris P. Hejblum
Published: 1 May 2021
Journal of Biomedical Informatics , Volume 117; doi:10.1016/j.jbi.2021.103746

Abstract: Electronic Health Records (EHRs) often lack reliable annotation of patient medical conditions. Phenorm, an automated unsupervised algorithm to identify patient medical conditions from EHR data, has been developed. 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 parametric 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 0.943 [0.940 ; 0.945] and 0.987 [0.983 ; 0.990]. Cross-validated AUPRC were respectively 0.754 [0.744 ; 0.763] and 0.299 [0.198 ; 0.403]. 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. It achieves significantly better performance than state-of-the-art unsupervised methods especially for chronic diseases.
Keywords: electronic health records / high-throughput phenotyping / phenotypic big data / precision medicine

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

Share this article

Click here to see the statistics on "Journal of Biomedical Informatics" .
References (17)
    Back to Top Top