Artificial Intelligence Predicts Cost After Ambulatory Anterior Cruciate Ligament Reconstruction.

ABSTRACT:

Purpose

To develop and internally validate a machine-learning algorithm to reliably predict cost after anterior cruciate ligament reconstruction (ACLR).

Methods

A retrospective review of the New York State Ambulatory Surgery and Services database was performed to identify patients who underwent elective ACLR from 2015 to 2016. Features included in initial models consisted of patient characteristics (age, sex, insurance status, income, medical comorbidities as classified by the Clinical Classifications Software diagnosis code) as well as intraoperative variables (type of anesthesia and procedure-specific factors). Models were generated to predict total charges using 4 algorithms: random forest, extreme gradient boost, elastic net penalized regression, and support vector machi

SUBMITTER: Lu Y 

PROVIDER: S-EPMC8689347 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature