Predicting Unplanned Readmissions Following a Hip or Knee Arthroplasty: Retrospective Observational Study.

ABSTRACT:

Background

Total joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models.

Objective

This study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirical

SUBMITTER: Mohammadi R 

PROVIDER: S-EPMC7732713 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature