Context-Sensitive Common Data Models for Genetic Rare Diseases - A Concept
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Current challenges of rare diseases need to involve patients, physicians, and the research community to generate new insights on comprehensive patient cohorts. Interestingly, the integration of patient context has been insufficiently considered, but might tremendously improve the accuracy of predictive models for individual patients. Here, we conceptualized an extension of the European Platform for Rare Disease Registration data model with contextual factors. This extended model can serve as an enhanced baseline and is well-suited for analyses using artificial intelligence models for improved predictions. The study is an initial result that will develop context-sensitive common data models for genetic rare diseases.
Details
Original language | English |
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Pages (from-to) | 139-140 |
Number of pages | 2 |
Journal | Studies in health technology and informatics |
Volume | 305 |
Publication status | Published - 29 Jun 2023 |
Peer-reviewed | Yes |
External IDs
ORCID | /0000-0002-9888-8460/work/142254106 |
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ORCID | /0000-0002-1887-4772/work/143075278 |
Scopus | 85164231145 |
ORCID | /0000-0002-5577-7760/work/153152098 |
Keywords
Research priority areas of TU Dresden
Subject groups, research areas, subject areas according to Destatis
ASJC Scopus subject areas
Keywords
- Common Data Model, Context-Sensitive, Rare Disease, Physicians, Artificial Intelligence, Humans, Problem Solving, Rare Diseases/genetics