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.
|Title of host publication||Bioinformatics and Biomedical Engineering - 5th International Work-Conference, IWBBIO 2017, Proceedings|
|Editors||Ignacio Rojas, Francisco Ortuno|
|Number of pages||14|
|State||Published - 2017|
|Event||5th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017 - Granada, Spain|
Duration: Apr 26 2017 → Apr 28 2017
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||5th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017|
|Period||4/26/17 → 4/28/17|
Bibliographical noteFunding Information:
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.
© Springer International Publishing AG 2017.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science (all)