Data-Driven Algorithms for Classification of In- and Outpatients in the Danish National Patient Register

Our study shows that all presented classification methods are suitable for categorizing patient types in DNPR2 depending on the available data and furthermore demonstrated robustness, supporting their suitability for classification in DNPR3. Future research should explore advanced techniques and comprehensive department classification for enhanced accuracy and applicability.

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Publication Details

Journal
Clinical Epidemiology
Published
2025-02-01
DOI
https://doi.org/10.2147/clep.s500800
Citations
1
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
2.48
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article

Data-Driven Algorithms for Classification of In- and Outpatients in the Danish National Patient Register

Ann‐Sophie Buchardt, Andreas Jensen, Pi Vejsig Madsen
1 citations
Clinical Epidemiology
Machine Learning in Healthcare
2.48
article

Data-Driven Algorithms for Classification of In- and Outpatients in the Danish National Patient Register

Ann‐Sophie Buchardt, Andreas Jensen, Pi Vejsig Madsen
article en
1 citations

Abstract

Our study shows that all presented classification methods are suitable for categorizing patient types in DNPR2 depending on the available data and furthermore demonstrated robustness, supporting their suitability for classification in DNPR3. Future research should explore advanced techniques and comprehensive department classification for enhanced accuracy and applicability.

Clinical EpidemiologyVol. Volume 17
Copenhagen University Hospital (DK), Rigshospitalet (DK)
Openalex Percentile: Top 12%
Machine Learning in Healthcare
2.48
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