ArticleJournal of orthopaedic surgery and research2023
Machine learning algorithms to predict risk of postoperative pneumonia in elderly with hip fracture.
Article in Journal of orthopaedic surgery and research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Artificial Intelligence and Machine Learning for Outcome Prediction After Osteoporotic Hip Fracture: A Systematic Review and Meta-analysis of Prediction Model Performance.Current osteoporosis reports · 2026Pooled it
- Article
- Elevated neutrophil percentage-to-albumin ratio is associated with postoperative pneumonia in patients with hip fractures.Frontiers in medicine · 2026Article
- MMPro-HIP: multimodal progressive fusion model for elderly HIP fracture risk prediction.Frontiers in medicine · 2026Article
- Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures.Scientific reports · 2025Article
- Developing a nomogram for postoperative delirium in elderly patients with hip fractures.World journal of psychiatry · 2025Article
- Application of artificial intelligence in postoperative orthopedic rehabilitation: a scoping review.Frontiers in digital health · 2025Review
- A nomogram for one-year risk of death after hip fracture.Frontiers in medicine · 2025Article
- Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture.Clinical interventions in aging · 2025Article
- Development and validation of machine learning models to predict perioperative transfusion risk for hip fractures in the elderly.Annals of medicine · 2024Article
- A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?Journal of multidisciplinary healthcare · 2024Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundHip fracture (HF) is one of the most common fractures in the elderly and is significantly associated with high mortality and unfavorable prognosis. Postoperative pneumonia (POP), the most common postoperative complication of HF, can seriously affect patient prognosis and increase the burden on the healthcare system. The aim of this study was to develop machine learning models for identifying elderly patients at high risk of pneumonia after hip fracture surgery.
methodsFrom May 2016 to November 2022, patients admitted to a single central hospital for HF served as the study population. We extracted data that could be collected within 24 h of patient admission. The dataset was divided into training and validation sets according to 70:30. Based on the screened risk factors, prediction models were developed using seven machine learning algorithms, namely CART, GBM, KNN, LR, NNet, RF, and XGBoost, and their performance was evaluated.
resultsEight hundred five patients were finally included in the analysis and 75 (9.3%) patients suffered from POP. Age, CI, COPD, WBC, HB, GLU, STB, GLOB, Ka
conclusionThe machine learning model we created is ideal for detecting elderly patients at high risk of POP after HF at an early stage.
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