Publications by authors named "Dmitry Dymshyts"
Article Synopsis
- OHDSI (Observational Health Data Sciences and Informatics) is a massive distributed data network with over 331 sources and 2.1 billion patient records, facilitating large-scale observational research through standardized data.
- The OHDSI Standardized Vocabularies, a crucial component of this network, include more than 10 million concepts from 136 vocabularies, allowing for better data harmonization and easier research execution.
- This open-source vocabulary system addresses challenges in observational research, enabling various analyses such as efficient phenotyping and patient-level predictions, and encourages researchers to utilize and contribute to its ongoing development.
Objectives: We incorporated the Korean Electronic Data Interchange (EDI) vocabulary into Observational Medical Outcomes Partnership (OMOP) vocabulary using a semi-automated process. The goal of this study was to improve the Korean EDI as a standard medical ontology in Korea.
Methods: We incorporated the EDI vocabulary into OMOP vocabulary through four main steps.
Article Synopsis
- The study investigated the safety of hydroxychloroquine, a drug used for rheumatoid arthritis, particularly considering its controversial use for COVID-19 pneumonia.
- It compared adverse events in patients starting hydroxychloroquine versus those starting sulfasalazine, analyzing data from multiple countries and focusing on severe events within a 30-day period.
- Results showed no significant increase in severe adverse events for short-term use of hydroxychloroquine compared to sulfasalazine, but long-term use may be linked to a higher risk of cardiovascular issues.
Standardized Observational Cancer Research Using the OMOP CDM Oncology Module.
Stud Health Technol Inform
August 2019
Observational research in cancer requires substantially more detail than most other therapeutic areas. Cancer conditions are defined through histology, affected anatomical structures, staging and grading, and biomarkers, and are treated with complex therapies. Here, we show a new cancer module as part of the OMOP CDM, allowing manual and automated abstraction and standardized analytics.
View Article and Find Full Text PDFSystematic application of observational data to the understanding of impacts of cancer treatments requires detailed information models allowing meaningful comparisons between treatment regimens. Unfortunately, details of systemic therapies are scarce in registries and data warehouses, primarily due to the complex nature of the protocols and a lack of standardization. Since 2011, we have been creating a curated and semi-structured website of chemotherapy regimens, HemOnc.
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