ArticleBMC medical informatics and decision making2010
Regression tree construction by bootstrap: model search for DRG-systems applied to Austrian health-data.
Article in BMC medical informatics and decision making, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A systematic review of the prediction of hospital length of stay: Towards a unified framework.PLOS digital health · 2022Article
- DRG grouping by machine learning: from expert-oriented to data-based method.BMC medical informatics and decision making · 2021Article
- Risk factors associated with postoperative complications after liver cancer resection surgery in western China.Cost effectiveness and resource allocation : C/E · 2021Article
- Cost Control of Treatment for Cerebrovascular Patients Using a Machine Learning Model in Western China.Journal of healthcare engineering · 2021Article
- Effect of Occupational Health and Safety Training for Chinese Construction Workers Based on the CHAID Decision Tree.Frontiers in public health · 2021Article
- Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model.World journal of clinical cases · 2020Article
- Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data.Computational and mathematical methods in medicine · 2016Article
- Exploring the transition to DRGs in Developing Countries: A case study in Shanghai, China.Pakistan journal of medical sciences · 2014Article
- A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer.BMC medical informatics and decision making · 2013Article
- Reducing the Risk of Upcoding in DRG Grouping Through a Two-Stage DRG Grouper Based on Machine Learning.Inquiry : a journal of medical care organization, provision and financingArticle
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3 authors.
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Abstract
backgroundDRG-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.
methodsDespite 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.
resultsThe 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.
conclusionsBumping 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.
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