Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture.

Journal: Clinical interventions in aging

PMID: 40034472

Abstract

BACKGROUND: Hip fractures have become a significant health concern, particularly among super-aged patients, who were at a high risk of postoperative pneumonia due to their frailty and the presence of multiple comorbidities. This study aims to establish and validate a model to predict postoperative pneumonia among super-aged patients with hip fracture.

Authors

Miaotian Tang

Department of Trauma Orthopaedics, Peking University People's Hospital, Beijing, 100044, People's Republic of China.
Meng Zhang

College of Software, Beihang University, Beijing, China.
Yu Dang

Department of Mechanical Engineering, The University of Auckland, Auckland, New Zealand.
Mingxing Lei

Department of Orthopedic Surgery, Hainan Hospital of Chinese People's Liberation Army (PLA) General Hospital, Sanya, China.
Dianying Zhang

Department of Trauma Orthopaedics, Peking University People's Hospital, Beijing, 100044, People's Republic of China.

Keywords

Aged, 80 and over Algorithms China Female Hip Fractures Humans Incidence Logistic Models Machine Learning Male Neural Networks, Computer Pneumonia Postoperative Complications Risk Assessment Risk Factors Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (40034472)

Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture.

Abstract

Authors

Keywords

External Resources

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