Authors:
Dmitriy Babichenko
1
;
Behnam Rahdari
1
;
Ben Stein
1
;
Suraj Subramanian
1
;
Claudia Ramos Rivers
2
;
Gong Tang
3
and
David Binion
2
Affiliations:
1
School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, U.S.A.
;
2
School of Public Health, University of Pittsburgh, Pittsburgh, PA, U.S.A.
;
3
School of Medicine, University of Pittsburgh, Pittsburgh, PA, U.S.A.
Keyword(s):
Inflammatory Bowel Disease, Healthcare Utilization, Machine Learning, Classification, Deep Learning, Clinical Decision Support Systems.
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.
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