Rare diseases and disabilities: improving the information available with three Orphanet projects

Chalendar, Myriam de; Daniel, M. T.; Olry, Annie; Rath, Ana

doi:10.1186/1750-1172-9-s1-o31

Cited by 6 publications

(3 citation statements)

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“…The large number of RDs that affect vulnerable populations, especially young children; and 4. RDs may be associated with significant, often progressive, disability and cognitive impairments [23].…”

Section: Documents Including the European Medicines Agency Guideline On Clinical Trials In Small Populations In 2006mentioning

confidence: 99%

Patient-Reported Outcome and Observer-Reported Outcome Assessment in Rare Disease Clinical Trials: An ISPOR COA Emerging Good Practices Task Force Report

Benjamin¹,

et al. 2017

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Section: Documents Including the European Medicines Agency Guideline On Clinical Trials In Small Populations In 2006mentioning

confidence: 99%

Patient-Reported Outcome and Observer-Reported Outcome Assessment in Rare Disease Clinical Trials: An ISPOR COA Emerging Good Practices Task Force Report

Benjamin¹,

et al. 2017

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“…We have included nine groups of diseases from the endocrinology domain, two groups from gastroenterology, four from pneumonology, and one from the hematology domain. The list of diseases and their respective ICD-10-GM and Orpha codes (45) are shown in Table 2.…”

Section: Use Case-speci C Data Modelsmentioning

confidence: 99%

How to customize Common Data Models for rare diseases: an OMOP-based implementation and lessons learned

Ahmadi,

Zoch,

Guengoeze

et al. 2023

Preprint

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Background Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common Data Models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. Methods In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. Results We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. Discussion This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. Conclusion The customized data structure related our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

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“…Orphanet 2 , the international database and portal on Rare Diseases (RD) and orphan drugs, is collecting information about the functional consequences associated to rare diseases [15]. It uses the Orphanet Functioning Thesaurus, derived and adapted from the International Classification of Functioning, Disability and Health -Children and Youth (ICF-CY [16]).…”

Section: Related Workmentioning

confidence: 99%

Understanding and Improving Disability Identification in Medical Documents

2020

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Disabilities are a problem that affects a large number of people in the world. Gathering information about them is crucial to improve the daily life of the people who suffer from them but, since disabilities are often strongly associated with different types of diseases, the available data are widely dispersed. In this work we review existing proposal for the problem, making an in-depth analysis, and from it we make a proposal that improves the results of previous systems. The analysis focuses on the results of the participants in DIANN shared task was proposed (IberEval 2018), devoted to the detection of named disabilities in electronic documents. In order to evaluate the proposed systems using a common evaluation framework, a corpus of documents, in both English and Spanish, was gathered and annotated. Several teams participated in the task, either using classic methods or proposing specific approaches to deal effectively with the complexities of the task. Our aim is to provide insight for future advances in the field by analyzing the participating systems and identifying the most effective approaches and elements to tackle the problem. We have validated the lessons learned from this analysis through a new proposal that includes the most promising elements used by the participating teams. The proposed system improves, for both languages, the results obtained during the task.

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Rare diseases and disabilities: improving the information available with three Orphanet projects

Cited by 6 publications

References 0 publications

Patient-Reported Outcome and Observer-Reported Outcome Assessment in Rare Disease Clinical Trials: An ISPOR COA Emerging Good Practices Task Force Report

Patient-Reported Outcome and Observer-Reported Outcome Assessment in Rare Disease Clinical Trials: An ISPOR COA Emerging Good Practices Task Force Report

How to customize Common Data Models for rare diseases: an OMOP-based implementation and lessons learned

Understanding and Improving Disability Identification in Medical Documents

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