Algorithmic Prediction of Health-Care Costs

By Dimitris J. Bertsimas, M. Bjarnadóttir, Michael Kane, J. Christian Kryder, R. Pandey, Santosh Srinivas Vempala, Grant J. Wang. 2008

207 citations recorded by the source corpus.

How researchers see this paper

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Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, background): “See Carrizosa and Romero Morales (2013) for a recent review on Mathematical Optimization and the SVM, and Bertsimas et al. (2008), Brooks (2011), Cecchini et al.”
A survey on Data Mining approaches for Healthcare (2013, methodology): “used classification tree approach to predict the cost of healthcare [38] by using the dataset of 3 years collected from the insurance companies to perform the experiment.”
Prediction of hospitalization due to heart diseases by supervised learning methods (2014, unknown): “[7] combine spectral clustering (unsupervised method) with classification trees (supervised method) to first group similar patients into clusters and then make more accurate predictions about the near-future health-care cost.”
Supervised classification and mathematical optimization (2013, background): “…fraud detection [87], customer targeting [73], customer loyalty [106] [112], market basket analysis [61], recommender systems [62], revenue management [122], services booking cancellations [203], country risk ratings [114], prediction of health costs [24] or stock market forecast [103] [156].”
Data Mining in Healthcare and Biomedicine: A Survey of the Literature (2012, methodology): “Bertsimas and colleagues performed a comparative research study using two data mining algorithms for prediction of the health care cost [ 91 ].”