International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Vietnam.
Front Public Health. 2023 Jun 20;11:1164820. doi: 10.3389/fpubh.2023.1164820. eCollection 2023.
Age-specific risk factors may delay posttraumatic functional recovery; complex interactions exist between these factors. In this study, we investigated the prediction ability of machine learning models for posttraumatic (6 months) functional recovery in middle-aged and older patients on the basis of their preexisting health conditions.
Data obtained from injured patients aged ≥45 years were divided into training-validation ( = 368) and test ( = 159) data sets. The input features were the sociodemographic characteristics and baseline health conditions of the patients. The output feature was functional status 6 months after injury; this was assessed using the Barthel Index (BI). On the basis of their BI scores, the patients were categorized into functionally independent (BI >60) and functionally dependent (BI ≤60) groups. The permutation feature importance method was used for feature selection. Six algorithms were validated through cross-validation with hyperparameter optimization. The algorithms exhibiting satisfactory performance were subjected to bagging to construct stacking, voting, and dynamic ensemble selection models. The best model was evaluated on the test data set. Partial dependence (PD) and individual conditional expectation (ICE) plots were created.
In total, nineteen of twenty-seven features were selected. Logistic regression, linear discrimination analysis, and Gaussian Naive Bayes algorithms exhibited satisfactory performances and were, therefore, used to construct ensemble models. The k-Nearest Oracle Elimination model outperformed the other models when evaluated on the training-validation data set (sensitivity: 0.732, 95% CI: 0.702-0.761; specificity: 0.813, 95% CI: 0.805-0.822); it exhibited compatible performance on the test data set (sensitivity: 0.779, 95% CI: 0.559-0.950; specificity: 0.859, 95% CI: 0.799-0.912). The PD and ICE plots showed consistent patterns with practical tendencies.
Preexisting health conditions can predict long-term functional outcomes in injured middle-aged and older patients, thus predicting prognosis and facilitating clinical decision-making.
特定年龄的风险因素可能会延迟创伤后的功能恢复；这些因素之间存在复杂的相互作用。在这项研究中，我们根据中年和老年患者的既往健康状况，研究了基于机器学习模型对创伤后（6 个月）功能恢复的预测能力。
从年龄≥45 岁的受伤患者中获得的数据分为训练-验证（n=368）和测试（n=159）数据集。输入特征为患者的社会人口统计学特征和基线健康状况。输出特征是受伤后 6 个月的功能状态；使用巴氏指数（BI）进行评估。根据 BI 评分，患者分为功能独立（BI＞60）和功能依赖（BI≤60）两组。采用置换特征重要性方法进行特征选择。通过超参数优化的交叉验证验证了六种算法。对表现良好的算法进行袋装处理，构建堆叠、投票和动态集成选择模型。在测试数据集上评估最佳模型。创建了部分依赖（PD）和个体条件期望（ICE）图。
共有 27 个特征中的 19 个被选中。逻辑回归、线性判别分析和高斯朴素贝叶斯算法表现良好，因此被用于构建集成模型。在训练-验证数据集上评估时，k-Nearest Oracle Elimination 模型的性能优于其他模型（灵敏度：0.732，95%置信区间：0.702-0.761；特异性：0.813，95%置信区间：0.805-0.822）；在测试数据集上也表现出了兼容的性能（灵敏度：0.779，95%置信区间：0.559-0.950；特异性：0.859，95%置信区间：0.799-0.912）。PD 和 ICE 图显示出与实际趋势一致的模式。
既往健康状况可预测中年和老年受伤患者的长期功能结局，从而预测预后并有助于临床决策。
