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Abstract Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Abstract Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68–99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

Aim: To describe the complex roles of and crucial support provided by caregivers to young adults with cerebral palsy (CP) during the transition to adulthood. Method: This was a qualitative study. We conducted 20 semi-structured interviews (13 caregiver; 7 patient/caregiver dyad) and analyzed data using a qualitative descriptive approach informed by phenomenological principles. Results: We identified four major themes related to the roles that caregivers play during the transition to adulthood for young adults with CP: 1) caregivers as care coordinators; 2) navigating logistic and insurance barriers; 3) adjusting to adulthood and planning for an uncertain future; and 4) supporting maintenance of health, function, and quality of life. Overall, caregivers shared the many different ways that they act as advocates for young adults with CP. Many participants identified the extensive amount of time, energy, and financial resources required to appropriately provide care. Conclusions: The transition to adulthood for young adults with CP is a challenging, uncertain, complex process for both adults with CP and their caregivers. Throughout this transition, caregivers also prioritize the happiness and quality of life of their adult children with CP. Our findings could be used by clinicians and researchers to develop and study patient- and family-centered transition processes for individuals with CP that attend to the struggles and priorities of youth with CP and caregivers alike.