Abstract
Background: Huge amounts of data are collected by healthcare providers
and other institutions. However, there are data protection regulations, which limit
their utilisation for secondary use, e.g. research. In scenarios, where several data
sources are obtained without universal identifiers, record linkage methods need to
be applied to obtain a comprehensive dataset. Objectives: In this study, we had the
objective to link two datasets comprising data from ergometric performance tests in
order to have reference values to free text annotations for assessing their data quality.
Methods: We applied an iterative, distance-based time series record linkage
algorithm to find corresponding entries in the two given datasets. Subsequently, we
assessed the resulting matching rate. The implementation was done in Matlab.
Results: The matching rate of our record linkage algorithm was 74.5% for matching
patients´ records with their ergometry records. The highest rate of appropriate free
text annotations was 87.9%. Conclusion: For the given scenario, our algorithm
matched 74.5% of the patients. However, we had no gold standard for validating our
results. Most of the free text annotations contained the expected values.
Originalsprache | Englisch |
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Titel | dHealth 2019 - From eHealth to dHealth |
Redakteure/-innen | Dieter Hayn, Alphons Eggerth, Karl Kreiner, Sai Veeranki, Heimo Traninger, Robert Modre-Osprian, Günter Schreier |
Seiten | 210-217 |
Seitenumfang | 8 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2019 |
Veranstaltung | dHealth 2019 - 13th annual conference on health informatics meets digital health - Dauer: 28 Mai 2019 → 29 Mai 2019 |
Konferenz
Konferenz | dHealth 2019 - 13th annual conference on health informatics meets digital health |
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Zeitraum | 28/05/19 → 29/05/19 |
Research Field
- Exploration of Digital Health
Schlagwörter
- medical record linkage
- data analysis
- ergometry
- exercise test
- cardiac rehabilitation