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Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb
Yiqun Chen1,2, Mingxuan Ma1, Dandan Qu3
1Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, China.
Insights
Machine learning models like XGBoost and Transformer accurately predict postoperative pneumonia after lower limb fracture surgery. Early identification aids in preventing this common complication and improving patient outcomes.
Area of Science:
- Orthopedics
- Pulmonology
- Data Science
Background:
- Postoperative pneumonia is a common complication following lower limb fracture surgery.
- It leads to extended hospital stays and increased mortality rates.
- Early detection and prevention are vital for patient recovery.
Purpose of the Study:
- To identify clinical indicators for predicting postoperative pneumonia in lower limb fracture patients.
- To evaluate the efficacy of machine learning and deep learning models in forecasting pneumonia risk.
Main Methods:
- A retrospective analysis of patients undergoing lower limb fracture surgery from 2016-2023.
- Classification of patients into case (pneumonia) and control (no pneumonia) groups.
- Application of machine learning (XGBoost) and deep learning (Transformer) algorithms for prediction.
Main Results:
- Key predictors identified include Age, Gender, Fracture type, Venous Thromboembolism (VTE), Hypertension, Chronic Obstructive Pulmonary Disease (COPD), Cancer, Atrial Fibrillation, Cerebrovascular Disease, Hypoalbuminemia, Free Fatty Acid, Albumin, Albumin to Globulin Ratio, Calcium, Fibrinogen, D-dimer, Alcohol, Surgical Grade, and C-reactive Protein.
- XGBoost achieved an AUC of 0.866 and F1 score of 0.807.
- Transformer models demonstrated superior performance with an AUC of 0.946 and F1 score of 0.889.
Conclusions:
- XGBoost and Transformer models show significant potential for predicting postoperative pneumonia in lower extremity fracture patients.
- These models can aid in the early prevention and treatment strategies.
- Implementing proactive health management can reduce the risk of postoperative pneumonia.
Abstract:
Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can significantly prolong hospitalization periods and elevate mortality rates. Consequently, early prevention and identification of this condition are crucial in improving patient prognosis. In this study, clinical indicators pertaining to postoperative pneumonia in patients with lower limb fractures at Nantong University Hospital, spanning the years 2016 to 2023, were subjected to a analysis. The patients who encountered postoperative pneumonia subsequent to their lower limb fracture surgeries during hospitalization were categorized as the case group, whereas those who did not develop such a complication served as the control group. To forecast the likelihood of postoperative pneumonia occurrence, both machine learning and deep learning algorithms were employed. The study identified Age, Gender, Fracture type, Venous thromboembolism (VTE), Hypertension, Chronic obstructive pulmonary disease (COPD), Cancer, Atrial fibrillation, Cerebrovascular disease, Hypoalbuminemia, Free fatty acid, Albumin, Albumin to globulin ratio, Calcium, Fibrinogen, D-dimer, Alcohol, Surgical grade and C-reactive protein as significant predictors of postoperative pneumonia. XGBoost and Transformer models have better performance (AUC 0.866 VS 0.946, F1 0.807 VS 0.889), and both models have better substantial prediction ability for the occurrence of postoperative pneumonia. In conclusion, XGBoost and Transformer models serve as potential tools for the prevention and treatment of postoperative pneumonia in patients with lower-extremity fractures. By adopting appropriate health management practices, the risk of developing postoperative pneumonia in this patient population may be reduced.
