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.
Original language | English |
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Title of host publication | Bioinformatics and Biomedical Engineering - 5th International Work-Conference, IWBBIO 2017, Proceedings |
Editors | Ignacio Rojas, Francisco Ortuno |
Pages | 382-395 |
Number of pages | 14 |
DOIs | |
State | Published - 2017 |
Event | 5th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017 - Granada, Spain Duration: Apr 26 2017 → Apr 28 2017 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10209 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 5th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017 |
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Country/Territory | Spain |
City | Granada |
Period | 4/26/17 → 4/28/17 |
Bibliographical note
Publisher Copyright:© Springer International Publishing AG 2017.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science