Abstract
We applied and developed several machine learning techniques, including linear model, tree-based model, and deep neural networks to forecast expenditures for high utilizers in a very large public health program. The results show promise for predicting health care expenditures for these high utilizers. To improve interpretability, we quantified the contributions of influential input variables to the prediction score. These results help to advance the field toward targeted preventive care to lower overall health care costs.
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Acknowledgments
This work was supported in part by Texas HHSC and in part through Patient-Centered Outcomes Research Institute (PCORI) (PCO-COORDCTR2013) for development of the National Patient-Centered Clinical Research Network, known as PCORnet. The views, statements and opinions presented in this work are solely the responsibility of the author(s) and do not necessarily represent the views of the Texas HHSC and Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee or other participants in PCORnet.
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Yang, C., Delcher, C., Shenkman, E., Ranka, S. (2017). Machine Learning Approaches for Predicting High Utilizers in Health Care. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10209. Springer, Cham. https://doi.org/10.1007/978-3-319-56154-7_35
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DOI: https://doi.org/10.1007/978-3-319-56154-7_35
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