ArticleJAMIA open2022
Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model.
Article in JAMIA open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Association between Diagnostic History and Cancer Incidence within 5 Years: A Real-world Observational Analysis.Cancer research communications · 2026Observational
- ICU Length of Stay Patterns and In-Hospital Mortality: Clinical Determinants in a Tertiary-Care Hospital.Healthcare (Basel, Switzerland) · 2026Article
- DiNetxify-a python package for three‑dimensional disease network analysis based on electronic health record data.European journal of epidemiology · 2026Article
- The Estonian Biobank's journey from biobanking to personalized medicine.Nature communications · 2025Review
- The challenges of national health data ecosystems in feeding the European health data space: the Italian example.Frontiers in medicine · 2025Article
- Limitations of NHIC claim code-based surveillance and the necessity of UDI implementation in Korea.Scientific reports · 2024Article
- Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned.JAMIA open · 2023Article
- Cancer Research Line (CAREL): Development of Expanded Distributed Research Networks for Prostate Cancer and Lung Cancer.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data. Materials and Methods: A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example. Results: As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations. Conclusions: For the first time, there is a complete software package for detecting disease trajectories in health data.
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Registered trials
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