Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?

Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?

Background: Super-utilizers represent approximately 5% of the population in the United States (U.S.) and yet they are responsible for over 50% of healthcare expenditures. Using characteristics of hospital service areas (HSAs) to predict utilization of resource intensive healthcare (RIHC) may offer a...

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Bibliographic Details
Main Authors: Ailawadi, K.L (Author), Brown, J.R (Author), Emond, J.A (Author), MacKenzie, T.A (Author), Ricket, I.M (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
adult
article
child health
consumer
demographics
emergency ward
health care cost
health care utilization
hospital patient
hospital running cost
hospital service
human
independent variable
least absolute shrinkage and selection operator
machine learning
pipeline
random forest
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