Machine learning approaches for predicting high utilizers in health care

Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

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 languageEnglish
Title of host publicationBioinformatics and Biomedical Engineering - 5th International Work-Conference, IWBBIO 2017, Proceedings
EditorsIgnacio Rojas, Francisco Ortuno
Pages382-395
Number of pages14
DOIs
StatePublished - 2017
Event5th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017 - Granada, Spain
Duration: Apr 26 2017Apr 28 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10209 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017
Country/TerritorySpain
City Granada
Period4/26/174/28/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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