Accurate patient mortality prediction enables effective risk stratification, leading to personalized treatment plans and improved patient outcomes. However, predicting mortality in healthcare remains a significant challenge, with existing studies often focusing on specific diseases or limited predictor sets. This study evaluates machine learning models for all-cause in-hospital mortality prediction using the MIMIC-III database, employing a comprehensive feature engineering approach. Guided by clinical expertise and literature, we extracted key features such as vital signs (e.g., heart rate, blood pressure), laboratory results (e.g., creatinine, glucose), and demographic information. The Random Forest model achieved the highest performance with an AUC of 0.94, significantly outperforming other machine learning and deep learning approaches. This demonstrates Random Forest's robustness in handling high-dimensional, noisy clinical data and its potential for developing effective clinical decision support tools. Our findings highlight the importance of careful feature engineering for accurate mortality prediction. We conclude by discussing implications for clinical adoption and propose future directions, including enhancing model robustness and tailoring prediction models for specific diseases.

翻译：准确的患者死亡率预测能够实现有效的风险分层，从而制定个性化治疗方案并改善患者预后。然而，在医疗保健领域预测死亡率仍然是一项重大挑战，现有研究通常侧重于特定疾病或有限的预测因子集。本研究利用MIMIC-III数据库，采用全面的特征工程方法，评估了用于全因院内死亡率预测的机器学习模型。在临床专业知识和文献的指导下，我们提取了关键特征，如生命体征（例如心率、血压）、实验室结果（例如肌酐、葡萄糖）和人口统计学信息。随机森林模型取得了最佳性能，AUC达到0.94，显著优于其他机器学习和深度学习方法。这证明了随机森林在处理高维、噪声临床数据方面的鲁棒性及其开发有效临床决策支持工具的潜力。我们的研究结果强调了精细的特征工程对于准确死亡率预测的重要性。最后，我们讨论了临床应用的启示，并提出了未来的研究方向，包括增强模型鲁棒性以及为特定疾病定制预测模型。