Automatic phenotyping of electronical health record: PheVis algorithm
| dc.rights.license | open | en_US |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | FERTE, Thomas | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | COSSIN, Sebastien
IDREF: 197817874 | |
| dc.contributor.author | SCHAEVERBEKE, Thierry | |
| dc.contributor.author | BARNETCHE, Thomas | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | JOUHET, Vianney | |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | HEJBLUM, Boris
ORCID: 0000-0003-0646-452X IDREF: 189970316 | |
| dc.date.accessioned | 2023-06-26T13:51:57Z | |
| dc.date.available | 2023-06-26T13:51:57Z | |
| dc.date.issued | 2021-03-19 | |
| dc.identifier.issn | 1532-0480 (Electronic) 1532-0464 (Linking) | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/182827 | |
| dc.description.abstractEn | 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. | |
| dc.language.iso | EN | en_US |
| dc.subject.en | Electronic health records | |
| dc.subject.en | High-throughput phenotyping | |
| dc.subject.en | Phenotypic big data | |
| dc.subject.en | Precision medicine | |
| dc.title.en | Automatic phenotyping of electronical health record: PheVis algorithm | |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1016/j.jbi.2021.103746 | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.identifier.pubmed | 33746080 | en_US |
| bordeaux.journal | Journal of Biomedical Informatics | en_US |
| bordeaux.page | 103746 | en_US |
| bordeaux.volume | 117 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.team | ERIAS | en_US |
| bordeaux.team | SISTM_BPH | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| hal.export | false | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Biomedical%20Informatics&rft.date=2021-03-19&rft.volume=117&rft.spage=103746&rft.epage=103746&rft.eissn=1532-0480%20(Electronic)%201532-0464%20(Linking)&rft.issn=1532-0480%20(Electronic)%201532-0464%20(Linking)&rft.au=FERTE,%20Thomas&COSSIN,%20Sebastien&SCHAEVERBEKE,%20Thierry&BARNETCHE,%20Thomas&JOUHET,%20Vianney&rft.genre=article |
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