Classification Models for Predicting Inflammatory Bowel Disease Healthcare Utilization

Dmitriy Babichenko, Behnam Rahdari, Ben Stein, Suraj Subramanian, Claudia Ramos Rivers, Gong Tang, David Binion

2022

Abstract

Objective. Inflammatory Bowel Disorders (IBD) is a group of gastric disorders that include well-known maladies such as Crohn’s disease and Ulcerative Colitis (UC), as well as a number of other gastric disorders that are not well classified. Subgroups of patients contribute disproportionately to treatment costs. This work aims to create and evaluate machine learning models designed to use demographic and clinical predictors of IBD to predict which patients would fall into the “high healthcare utilization” category. Materials and Methods. A series of machine learning models were trained on a dataset extracted from a prospective natural history registry from a tertiary IBD center and associated healthcare charges. The models were trained to predict which patients are likely to have the highest healthcare utilization charges (top 15%). Results. A gradient-boosted trees classification model (accuracy 0.898, AUC 0.748) performed best out of the 12 evaluated modeling approaches. Conclusion. Classification models such as the ones evaluated in this work provide a reasonable basis for a clinical decision support system designed to predict which IBD patients are likely to become high expenditure.

Paper Citation

in Harvard Style

Babichenko D., Rahdari B., Stein B., Subramanian S., Rivers C., Tang G. and Binion D. (2022). Classification Models for Predicting Inflammatory Bowel Disease Healthcare Utilization. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF; ISBN 978-989-758-552-4, SciTePress, pages 154-161. DOI: 10.5220/0010852100003123

in Bibtex Style

@conference{healthinf22,
author={Dmitriy Babichenko and Behnam Rahdari and Ben Stein and Suraj Subramanian and Claudia Ramos Rivers and Gong Tang and David Binion},
title={Classification Models for Predicting Inflammatory Bowel Disease Healthcare Utilization},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF},
year={2022},
pages={154-161},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010852100003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF
TI - Classification Models for Predicting Inflammatory Bowel Disease Healthcare Utilization
SN - 978-989-758-552-4
AU - Babichenko D.
AU - Rahdari B.
AU - Stein B.
AU - Subramanian S.
AU - Rivers C.
AU - Tang G.
AU - Binion D.
PY - 2022
SP - 154
EP - 161
DO - 10.5220/0010852100003123
PB - SciTePress