Enhancing Longitudinal Data Analysis with Unstructured EHRs: A Case Study of Renal Function Evaluation in Rare Disease.

Saved in:

Bibliographic Details
Title:	Enhancing Longitudinal Data Analysis with Unstructured EHRs: A Case Study of Renal Function Evaluation in Rare Disease.
Authors:	Wang X; Clinical Bioinformatics Group, Imagine Institute, Université Paris Cité, Inserm UMR 1163, Paris, France., Faviez C; Clinical Bioinformatics Group, Imagine Institute, Université Paris Cité, Inserm UMR 1163, Paris, France., Douillet M; Data Science Platform, Imagine Institute, Université Paris Cité, Inserm UMR 1163, Paris, France., Knebelmann B; Hôpital Necker-Enfants Malades, AP-HP, Paris, France., Garcelon N; Clinical Bioinformatics Group, Imagine Institute, Université Paris Cité, Inserm UMR 1163, Paris, France.; Data Science Platform, Imagine Institute, Université Paris Cité, Inserm UMR 1163, Paris, France., Burgun A; Clinical Bioinformatics Group, Imagine Institute, Université Paris Cité, Inserm UMR 1163, Paris, France.; Hôpital Necker-Enfants Malades, AP-HP, Paris, France., Chen X; Clinical Bioinformatics Group, Imagine Institute, Université Paris Cité, Inserm UMR 1163, Paris, France.; Data Science Platform, Imagine Institute, Université Paris Cité, Inserm UMR 1163, Paris, France.
Source:	Studies in health technology and informatics [Stud Health Technol Inform] 2025 May 15; Vol. 327, pp. 1270-1274.
Publication Type:	Journal Article
Journal Info:	Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	1879-8365
DOI:	10.3233/SHTI250602