Electronic health records for the diagnosis of rare diseases

Autor: Antoine Neuraz, Rémi Salomon, Anita Burgun, Nicolas Garcelon
Přispěvatelé: Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Université de Paris (UP), Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Service de néphrologie pédiatrique [CHU Necker], CCSD, Accord Elsevier, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Université Paris Cité - UFR Médecine [Santé] (UPCité UFR Médecine), Université Paris Cité (UPCité), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)
Rok vydání: 2020
Předmět:
MESH: Rare Diseases
0301 basic medicine
Decision support system
[INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO]
Computer science
media_common.quotation_subject
030232 urology & nephrology
MESH: Algorithms
Translational research
Reuse
Health records
Health informatics
Adaptability
Machine Learning
MESH: Natural Language Processing
03 medical and health sciences
0302 clinical medicine
pediatric nephrology
Electronic Health Records
Humans
MESH: Electronic Health Records
Repurposing
Natural Language Processing
media_common
education
MESH: Humans
MESH: Machine Learning
business.industry
rare diseases
electronic health record
[INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO]
artificial intelligence
Data science
Data warehouse
3. Good health
030104 developmental biology
[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie
Nephrology
[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
business
Algorithms
Zdroj: Kidney International
Kidney International, Nature Publishing Group, 2020, 97 (4), pp.676-686. ⟨10.1016/j.kint.2019.11.037⟩
ISSN: 0085-2538
1523-1755
Popis: International audience; 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.
Databáze: OpenAIRE
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