Clinical Practice Guidelines for Rare Diseases: The Orphanet Database

Pavan, Sonia; Rommel, Kathrin; Marquina, María Elena Mateo; Höhn, Sophie; Lanneau, Valérie; Rath, Ana

doi:10.1371/journal.pone.0170365

Cited by 135 publications

(131 citation statements)

References 18 publications

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“…Serbia is the member of Orphanet and there is a Serbian version of Orphanet website. Orphanet was established in France in 1997(Jezela‐Stanek et al, ; Pavan et al, ), and now provides the most comprehensive information on rare disease with a network of 40 countries. Specially assigned ORPHANET numbers allow the classification of rare diseases (Jezela‐Stanek et al, ), used alongside ICD‐10.…”

Section: Introductionmentioning

confidence: 99%

The needs of patients with rare disease in Serbia. Why do we need National Strategy for rare disease?

Joldic¹,

Todorović

Terzić-Šupić

2019

Health Soc Care Community

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The patients with rare diseases in Serbia face the difficulties in procurement of medications as the Health insurance fund does not cover reimbursement for some medications, they face difficulties in receiving proper diagnosis which makes their position specific and complex. In an attempt to provide more support for the patients with rare diseases, their families and caregivers the helpline for rare diseases was established in October 2015. The aim of this research was to identify, examine and systematise needs of helpline users and forms of assistance provided by the team from the free helpline. The research was designed as a cross‐sectional study and was conducted between October 2015 and December 2016. The electronic database of National Organization for rare disease in Serbia helpline users was used as a data source. The user was the person who contacted helpline (patient, relative, friend, physician, etc). The “need” refers to the reasons for addressing the helpline. Helpline users had 549 needs in total; about healthcare—236 (42.98%), social care—113 (20.58%), psychological support—56 (10.20%) and other—144 (26.22%). Services were provided by the lawyer—130 (23.70%), social worker—71 (12.93%), Psychologist—56 (10.20%) and by all employees—292 (53.19%). The most common need for legal assistance among needs on healthcare was on legal aspects of access to and reimbursement of expenses for medications (32/74%–43.24%) and procedures for reimbursement of treatment abroad (11/74%–14.86%). The problems of patients with rare diseases and their families result primarily from the lack of relevant information and knowledge, as well as the non‐recognition of rare diseases in the laws and regulations of health and social care. Some problems can simply be solved by legal changes and by a better organisation and do not require additional funding. Only by adopting and implementing the National Strategy and Action Plan for Rare Diseases, the greatest number of problems and needs of people with rare diseases can be systematised and solved.

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Section: Introductionmentioning

confidence: 99%

The needs of patients with rare disease in Serbia. Why do we need National Strategy for rare disease?

Joldic¹,

Todorović

Terzić-Šupić

2019

Health Soc Care Community

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“…To develop our method, we made use of the manually-curated fraction of DisGeNet v5.0 that includes manually-curated associations from ORPHANET (Pavan et al, 2017), PSYGENET (Gutiérrez-Sacristán et al, 2015), UNIPROT (Consortium et al, 2018) and CTD (Davis et al, 2018). This dataset included 11,721 abstracts with 16,441 non-redundant gene-disease associations.…”

Section: Datamentioning

confidence: 99%

GeDex: A consensus Gene-disease Event Extraction System based on frequency patterns and supervised learning

Lm¹,

Olayo-Alarcón²,

et al. 2019

Preprint

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Motivation:The genetic mechanisms involved in human diseases are fundamental in biomedical research. Several databases with curated associations between genes and diseases have emerged in the last decades. Although, due to the demanding and time consuming nature of manual curation of literature, they still lack large amounts of information. Current automatic approaches extract associations by considering each abstract or sentence independently. This approach could potentially lead to contradictions between individual cases. Therefore, there is a current need for automatic strategies that can provide a literature consensus of gene-disease associations, and are not prone to making contradictory predictions.Results: Here, we present GeDex, an effective and freely available automatic approach to extract consensus gene-disease associations from biomedical literature based on a predictive model trained with four simple features. As far as we know, it is the only system that reports a single consensus prediction from multiple sentences supporting the same association. We tested our approach on the curated fraction of DisGeNet (f-score 0.77) and validated it on a manually curated dataset, obtaining a of 1 28 competitive performance when compared to pre-existing methods (f-score 0.74). In addition, we effectively recovered associations from an article collection of chronic pulmonary diseases and discovered that a large proportion is not reported in current databases. Our results demonstrate that GeDex, despite its simplicity, is a competitive tool that can successfully assist the curation of existing databases.

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“…Definitive diagnosis of a known Mendelian disorder is accomplished by matching the patient's genotype and phenotype to previously described cases from the literature. Manually curated databases (6)(7)(8)(9)(10) are utilized to more efficiently access extracts of the unstructured knowledge in the primary literature. Automatic gene ranking tools (11)(12)(13)(14)(15)(16)(17)(18) use these databases to prioritize candidate genes in patients' genomes for their ability to explain patient phenotypes.…”

Section: Introductionmentioning

confidence: 99%

AMELIE 2 speeds up Mendelian diagnosis by matching patient phenotype & genotype to primary literature

Birgmeier

Haeussler

Deisseroth

et al. 2019

Preprint

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The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication/s supporting a single gene that best explains a patient's disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process. AMELIE parses all 29 million PubMed abstracts, downloads and further parses hundreds of thousands of full text articles in search of information supporting the causality and associated phenotypes of any published genetic variant. AMELIE then prioritizes patient candidate variants for their likelihood of explaining any patient's given set of phenotypes. Diagnosis of singleton patients (without relatives' exomes) is the most time-consuming scenario. AMELIE ranked the causative gene at the very top in 2/3 of 215 diagnosed singleton Mendelian patients. Evaluating only the top 11 AMELIE scored genes of 127 (median) candidate genes per patient results in rapid diagnosis for 90+% of cases. AMELIE-based evaluation of all cases is 3-19x more efficient than hand-curated database-based approaches. We replicate these results on a cohort of clinical cases from Stanford Children's Health and the Manton Center for Orphan DiseaseResearch. An analysis web portal with our most recent update, programmatic interface and code will be available at AMELIE.stanford.edu. A pilot run of the web portal has already served many thousands of job submissions from dozens of countries.

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Clinical Practice Guidelines for Rare Diseases: The Orphanet Database

Cited by 135 publications

References 18 publications

The needs of patients with rare disease in Serbia. Why do we need National Strategy for rare disease?

The needs of patients with rare disease in Serbia. Why do we need National Strategy for rare disease?

GeDex: A consensus Gene-disease Event Extraction System based on frequency patterns and supervised learning

AMELIE 2 speeds up Mendelian diagnosis by matching patient phenotype & genotype to primary literature

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