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Jauk, S; Kramer, D; Schulz, S; Leodolter, W.
Evaluating the Impact of Incorrect Diabetes Coding on the Performance of Multivariable Prediction Models.
Stud Health Technol Inform. 2018; 251: 249-252.


Co-Autor*innen der Med Uni Graz
Schulz Stefan

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Plum Analytics:
The use of electronic health records for risk prediction models requires a sufficient quality of input data to ensure patient safety. The aim of our study was to evaluate the influence of incorrect administrative diabetes coding on the performance of a risk prediction model for delirium, as diabetes is known to be one of the most relevant variables for delirium prediction. We used four data sets varying in their correctness and completeness of diabetes coding as input for different machine learning algorithms. Although there was a higher prevalence of diabetes in delirium patients, the model performance parameters did not vary between the data sets. Hence, there was no significant impact of incorrect diabetes coding on the performance for our model predicting delirium.
Find related publications in this database (using NLM MeSH Indexing)
Algorithms -
Data Accuracy -
Delirium -
Diabetes Mellitus -
Electronic Health Records -
Humans -
International Classification of Diseases - standards
Machine Learning -

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