Healthc Inform Res.  2017 Jul;23(3):226-232. 10.4258/hir.2017.23.3.226.

Hierarchical Genetic Algorithm and Fuzzy Radial Basis Function Networks for Factors Influencing Hospital Length of Stay Outliers

• Belderrar
• Hazzab A

• KMID: 2403313
• DOI: http://doi.org/10.4258/hir.2017.23.3.226

Abstract

OBJECTIVES
Controlling hospital high length of stay outliers can provide significant benefits to hospital management resources and lead to cost reduction. The strongest predictive factors influencing high length of stay outliers should be identified to build a high-performance prediction model for hospital outliers.
METHODS
We highlight the application of the hierarchical genetic algorithm to provide the main predictive factors and to define the optimal structure of the prediction model fuzzy radial basis function neural network. To establish the prediction model, we used a data set of 26,897 admissions from five different intensive care units with discharges between 2001 and 2012. We selected and analyzed the high length of stay outliers using the trimming method geometric mean plus two standard deviations. A total of 28 predictive factors were extracted from the collected data set and investigated.
RESULTS
High length of stay outliers comprised 5.07% of the collected data set. The results indicate that the prediction model can provide effective forecasting. We found 10 common predictive factors within the studied intensive care units. The obtained main predictive factors include patient demographic characteristics, hospital characteristics, medical events, and comorbidities.
CONCLUSIONS
The main initial predictive factors available at the time of admission are useful in evaluating high length of stay outliers. The proposed approach can provide a practical tool for healthcare providers, and its application can be extended to other hospital predictions, such as readmissions and cost.

Keyword

Data Mining; Intensive Care Units; Length of Stay; Machine Learning; Medical Informatics

MeSH Terms

Comorbidity
Data Mining
Dataset
Forecasting
Health Personnel
Humans
Intensive Care Units
Length of Stay*
Machine Learning
Medical Informatics
Methods

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