SNOMED CT Implementation

Højen, Anne Randorff; Gøeg, Kirstine Rosenbeck

doi:10.3414/me11-02-0023

Cited by 28 publications

(7 citation statements)

References 15 publications

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“…Using the method proposed by Frick et al [ 37 ], we considered diseases to be similar if they share a common ancestor within three generations. We used SNOMED-CT[ 38 ], a comprehensive and systematically organized ontology of medical terms, for disease similarity calculation. We found that 19 out of 22 disease categories can be mapped to 146 topics, except Developmental, Ear-Nose-Throat, and Respiratory.…”

Section: Resultsmentioning

confidence: 99%

“…To further systematically evaluate the similarity of diseases involved in each disease topic, we adopted three widely used disease ontologies (i.e., SNOMED-CT [ 38 ], Disease Ontology (DO) [ 40 ] and Human Phenotype Ontology (HPO) [ 41 ]) to investigate the semantic similarities between diseases within each topic as well as across topics. We defined that two diseases are related if they share the same ancestor nodes within three levels of the ontology hierarchy [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

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Systematic identification of latent disease-gene associations from PubMed articles

et al. 2018

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Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Systematic identification of latent disease-gene associations from PubMed articles

et al. 2018

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“…The use of a specific RD coding system, such as ORPHAcodes, as part of the patient’s electronic health record would allow for the burden of RDs in general practice to be measured [ 3 ]. Such an approach is being adopted elsewhere: in England, the move to a single terminology, SNOMED CT, was implemented across primary care and began to be deployed to GP in a phased approach from April 2018 [ 19 , 20 ]. Currently in Ireland, the implementation of the SNOMED CT has been agreed for retro-fit to GP software, which will facilitate more widespread and accurate recording of rare diseases.…”

Section: Discussionmentioning

confidence: 99%

The role of primary care in management of rare diseases in Ireland

Byrne¹,

Turner²,

Marron³

et al. 2020

Ir J Med Sci

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Background 'Slaintecare' aims to address complex patient care needs in an integrated fashion with an emphasis on patientcentred, patient-empowered community care.Currently there is a lack of knowledge of the impact of rare disease management in primary care and of the information tools required by general practitioners to deliver integrated care for rare disease patients. Aims To complete a pilot survey to estimate the general practice clinical workload attributable to selected rare diseases and assess the use of relevant information sources. Methods A retrospective cross-sectional survey was carried out of general practice consultations (2013-2017) for patients with 22 commonly recognised rare diseases. Results Around 31 general practitioners from 10 Irish practices completed information on 171 patients with rare diseases over 3707 consultations. General practice-specific coding systems were inadequate for rare disease patient identification. Over 139 (81.3%) patients were adult, and 32 (18.7%) were children. Management of care was hospital and not primary care based in 63%. Those eligible for state-reimbursed care had a significantly higher median number of consultations (23 consultations, IQR = 13-37, or 5.8 consultations/year) than those who paid privately (10 consultations, IQR = 4-19, or 2.5 consultations/year) (p < 0.005).General practitioners had access to public information resources on rare diseases but few had knowledge of (35.5%), or had ever used (12.9%) Orphanet, the international rare disease information portal. Conclusions Both specific rare disease-specific coding and use of the relevant rare disease information sources are lacking in general practice in Ireland.

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“…Several studies of EHR database entry practices stress the importance of ensuring the consistency of clinical data entered into the EHR in order to ensure that it contributes to better quality management, notably better clinical outcomes [33][34][35][36].…”

Section: Data Quality Of the Ehr Systemmentioning

confidence: 99%