Machine learning models predict total charges and drivers of cost for transcatheter aortic valve replacement.

ABSTRACT:

Background

Given the increasing healthcare costs, there is an interest in developing machine learning (ML) prediction models for estimating hospitalization charges. We use ML algorithms to predict hospitalization charges for patients undergoing transfemoral transcatheter aortic valve replacement (TF-TAVR) utilizing the National Inpatient Sample (NIS) database.

Methods

Patients who underwent TF-TAVR from 2012 to 2016 were included in the study. The primary outcome was total hospitalization charges. Study dataset was divided into 80% training and 20% testing sets. We used following ML regression algorithms: random forest, gradient boosting, k-nearest neighbors (KNN), multi-layer perceptron and linear regression. ML algorithms were built for for 3 stages: Stage 1, including var

SUBMITTER: Bansal A 

PROVIDER: S-EPMC9412209 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature