Electronic health records for the diagnosis of rare diseases - Archive ouverte HAL Access content directly
Journal Articles Kidney International Year : 2020

Electronic health records for the diagnosis of rare diseases

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

Abstract

With the emergence of electronic health records, the reuse of clinical data offers new perspectives for the diagnosis and management of patients with rare diseases. However, there are many obstacles to the repurposing of clinical data. The development of decision support systems depends on the ability to recruit patients, extract and integrate the patients' data, mine and stratify these data, and integrate the decision support algorithm into patient care. This last step requires an adaptability of the electronic health records to integrate learning health system tools. In this literature review, we examine the research that provides solutions to unlock these barriers and accelerate translational research: structured electronic health records and free-text search engines to find patients, data warehouses and natural language processing to extract phenotypes, machine learning algorithms to classify patients, and similarity metrics to diagnose patients. Medical informatics is experiencing an impellent request to develop decision support systems, and this requires ethical considerations for clinicians and patients to ensure appropriate use of health data.
Fichier principal
Vignette du fichier
S0085253820300120.pdf (14.44 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03476852 , version 1 (20-05-2022)

Licence

Attribution - NonCommercial - CC BY 4.0

Identifiers

Cite

Nicolas Garcelon, Anita Burgun, Rémi Salomon, Antoine Neuraz. Electronic health records for the diagnosis of rare diseases. Kidney International, 2020, 97 (4), pp.676-686. ⟨10.1016/j.kint.2019.11.037⟩. ⟨hal-03476852⟩
36 View
98 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More