Machine Learning Models Based on a National-Scale Cohort Identify Patients at High Risk for Prolonged Lengths of Stay Following Primary Total Hip Arthroplasty-Reference-Cited by-同舟云学术

Machine Learning Models Based on a National-Scale Cohort Identify Patients at High Risk for Prolonged Lengths of Stay Following Primary Total Hip Arthroplasty

Published:2023-10 Issue:10 Volume:38 Page:1967-1972
ISSN:0883-5403
Container-title:The Journal of Arthroplasty
language:en
Short-container-title:The Journal of Arthroplasty

Author:

Chen Tony Lin-Wei,Buddhiraju Anirudh,Costales Timothy G.,Subih Murad Abdullah,Seo Henry Hojoon,Kwon Young-Min

Publisher

Elsevier BV

Subject

Orthopedics and Sports Medicine

Reference36 articles.

1. Trends and determinants of length of stay and hospital reimbursement following knee and hip replacement: evidence from linked primary care and NHS hospital records from 1997 to 2014;Burn;BMJ Open,2018

2. The clinical and financial Consequences of the Centers for Medicare and Medicaid Services’ two-midnight rule in total joint arthroplasty;Schwartz;J Arthroplasty,2020

3. Limited influence of comorbidities on length of stay after total hip arthroplasty: experience of enhanced recovery after surgery;Ding;Orthop Surg,2020

4. Role of sociodemographic, co-morbid and intraoperative factors in length of stay following primary total hip arthroplasty;Inneh;J Arthroplasty,2015

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