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Regression tree construction by bootstrap: Model search for DRG-systems applied to Austrian health-data

Journal Article


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Abstract


  • Background. DRG-systems are used to allocate resources fairly to hospitals based on their performance. Statistically, this allocation is based on simple rules that can be modeled with regression trees. However, the resulting models often have to be adjusted manually to be medically reasonable and ethical. Methods. Despite the possibility of manual, performance degenerating adaptations of the original model, alternative trees are systematically searched. The bootstrap-based method bumping is used to build diverse and accurate regression tree models for DRG-systems. A two-step model selection approach is proposed. First, a reasonable model complexity is chosen, based on statistical, medical and economical considerations. Second, a medically meaningful and accurate model is selected. An analysis of 8 data-sets from Austrian DRG-data is conducted and evaluated based on the possibility to produce diverse and accurate models for predefined tree complexities. Results. The best bootstrap-based trees offer increased predictive accuracy compared to the trees built by the CART algorithm. The analysis demonstrates that even for very small tree sizes, diverse models can be constructed being equally or even more accurate than the single model built by the standard CART algorithm. Conclusions. Bumping is a powerful tool to construct diverse and accurate regression trees, to be used as candidate models for DRG-systems. Furthermore, Bumping and the proposed model selection approach are also applicable to other medical decision and prognosis tasks. © 2010 Grubinger et al; licensee BioMed Central Ltd.

Authors


  •   Grubinger, Thomas (external author)
  •   Kobel, Conrad
  •   Pfeiffer, Karl Peter. (external author)

Publication Date


  • 2010

Geographic Focus


Citation


  • Grubinger, T., Kobel, C. & Pfeiffer, K. Peter. (2010). Regression tree construction by bootstrap: Model search for DRG-systems applied to Austrian health-data. BMC Medical Informatics and Decision Making, 10 (1), 1-11.

Scopus Eid


  • 2-s2.0-77649089551

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1852&context=buspapers

Ro Metadata Url


  • http://ro.uow.edu.au/buspapers/848

Number Of Pages


  • 10

Start Page


  • 1

End Page


  • 11

Volume


  • 10

Issue


  • 1

Abstract


  • Background. DRG-systems are used to allocate resources fairly to hospitals based on their performance. Statistically, this allocation is based on simple rules that can be modeled with regression trees. However, the resulting models often have to be adjusted manually to be medically reasonable and ethical. Methods. Despite the possibility of manual, performance degenerating adaptations of the original model, alternative trees are systematically searched. The bootstrap-based method bumping is used to build diverse and accurate regression tree models for DRG-systems. A two-step model selection approach is proposed. First, a reasonable model complexity is chosen, based on statistical, medical and economical considerations. Second, a medically meaningful and accurate model is selected. An analysis of 8 data-sets from Austrian DRG-data is conducted and evaluated based on the possibility to produce diverse and accurate models for predefined tree complexities. Results. The best bootstrap-based trees offer increased predictive accuracy compared to the trees built by the CART algorithm. The analysis demonstrates that even for very small tree sizes, diverse models can be constructed being equally or even more accurate than the single model built by the standard CART algorithm. Conclusions. Bumping is a powerful tool to construct diverse and accurate regression trees, to be used as candidate models for DRG-systems. Furthermore, Bumping and the proposed model selection approach are also applicable to other medical decision and prognosis tasks. © 2010 Grubinger et al; licensee BioMed Central Ltd.

Authors


  •   Grubinger, Thomas (external author)
  •   Kobel, Conrad
  •   Pfeiffer, Karl Peter. (external author)

Publication Date


  • 2010

Geographic Focus


Citation


  • Grubinger, T., Kobel, C. & Pfeiffer, K. Peter. (2010). Regression tree construction by bootstrap: Model search for DRG-systems applied to Austrian health-data. BMC Medical Informatics and Decision Making, 10 (1), 1-11.

Scopus Eid


  • 2-s2.0-77649089551

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1852&context=buspapers

Ro Metadata Url


  • http://ro.uow.edu.au/buspapers/848

Number Of Pages


  • 10

Start Page


  • 1

End Page


  • 11

Volume


  • 10

Issue


  • 1