BMC medical informatics and decision making · 2026
Machine learning prediction of long-term postoperative pneumonia risk: a retrospective cohort study.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Lin Cheng-An, Sung Kuan-Lin, Lee Chun, Sung Sheng-Feng
Аннотация
BACKGROUND: Postoperative pneumonia is a significant complication, highlighting a patient's ongoing vulnerability. While traditional tools focus on short-term outcomes, the perioperative period offers a unique "stress test" window to identify high-risk patients. This study developed and validated a machine-learning-based prognostic framework to predict pneumonia risk up to one year after surgery.
METHODS: This retrospective study examined 11,655 surgical encounters at a tertiary hospital. Multiple machine learning algorithms, including random forest (RF), extreme gradient boosting, support vector machine, multilayer perceptron, and penalized logistic regression, were compared using 5-fold cross-validation. Class imbalance was handled using random oversampling (ROS) and undersampling. Models were tested on a separate set, and Shapley additive explanation (SHAP) analysis identified key predictors to improve clinical understanding.
RESULTS: Postoperative pneumonia occurred in 238 encounters (2.04%) within 365 days, peaking in the second postoperative month. The RF model with ROS (1:4 ratio) achieved the highest performance with an area under the receiver operating characteristic curve of 0.886, sensitivity of 85.4%, specificity of 77.4%, positive predictive value of 7.4%, and negative predictive value of 99.6%. SHAP analysis identified preoperative hemoglobin, European Society of Cardiology surgical risk, age, American Society of Anesthesiologists Physical Status class, and estimated glomerular filtration rate as key predictors of long-term vulnerability.
CONCLUSIONS: Machine learning facilitates prognostic stratification of patients at high risk of long-term vulnerability. By functioning as a high-sensitivity secondary screening tool, this model allows clinicians to safely "rule out" low-risk individuals and concentrate intensive surveillance and resources on the high-risk cohort, thereby improving long-term outcomes.
METHODS: This retrospective study examined 11,655 surgical encounters at a tertiary hospital. Multiple machine learning algorithms, including random forest (RF), extreme gradient boosting, support vector machine, multilayer perceptron, and penalized logistic regression, were compared using 5-fold cross-validation. Class imbalance was handled using random oversampling (ROS) and undersampling. Models were tested on a separate set, and Shapley additive explanation (SHAP) analysis identified key predictors to improve clinical understanding.
RESULTS: Postoperative pneumonia occurred in 238 encounters (2.04%) within 365 days, peaking in the second postoperative month. The RF model with ROS (1:4 ratio) achieved the highest performance with an area under the receiver operating characteristic curve of 0.886, sensitivity of 85.4%, specificity of 77.4%, positive predictive value of 7.4%, and negative predictive value of 99.6%. SHAP analysis identified preoperative hemoglobin, European Society of Cardiology surgical risk, age, American Society of Anesthesiologists Physical Status class, and estimated glomerular filtration rate as key predictors of long-term vulnerability.
CONCLUSIONS: Machine learning facilitates prognostic stratification of patients at high risk of long-term vulnerability. By functioning as a high-sensitivity secondary screening tool, this model allows clinicians to safely "rule out" low-risk individuals and concentrate intensive surveillance and resources on the high-risk cohort, thereby improving long-term outcomes.