What can millions of laboratory test results tell us about the temporal aspect of data quality? Study of data spanning 17 years in a clinical data warehouse
Computer Methods and Programs in Biomedicine, ISSN: 0169-2607, Vol: 181, Page: 104825
2019
- 10Citations
- 50Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations10
- Citation Indexes9
- CrossRef1
- Policy Citations1
- 1
- Captures50
- Readers50
- 50
Article Description
To identify common temporal evolution profiles in biological data and propose a semi-automated method to these patterns in a clinical data warehouse (CDW). We leveraged the CDW of the European Hospital Georges Pompidou and tracked the evolution of 192 biological parameters over a period of 17 years (for 445,000 + patients, and 131 million laboratory test results). We identified three common profiles of evolution: discretization, breakpoints, and trends. We developed computational and statistical methods to identify these profiles in the CDW. Overall, of the 192 observed biological parameters (87,814,136 values), 135 presented at least one evolution. We identified breakpoints in 30 distinct parameters, discretizations in 32, and trends in 79. our method allowed the identification of several temporal events in the data. Considering the distribution over time of these events, we identified probable causes for the observed profiles: instruments or software upgrades and changes in computation formulas. We evaluated the potential impact for data reuse. Finally, we formulated recommendations to enable safe use and sharing of biological data collection to limit the impact of data evolution in retrospective and federated studies (e.g. the annotation of laboratory parameters presenting breakpoints or trends).
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0169260718307089; http://dx.doi.org/10.1016/j.cmpb.2018.12.030; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85059432444&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/30612785; https://linkinghub.elsevier.com/retrieve/pii/S0169260718307089; https://dx.doi.org/10.1016/j.cmpb.2018.12.030
Elsevier BV
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