用新模型预测心脏手术后并发症，准确率超现有方法。
A Novel Generative Multi-Task Representation Learning Approach for Predicting Postoperative Complications in Cardiac Surgery Patients
- 设计surgVAE模型，通过跨任务跨队列学习挖掘患者数据内在规律。
- 在6类并发症预测中AUPRC达0.409，比最优对比模型高3.4%。
- 适合临床风险预测与可解释性需求高的医疗AI研究者使用。
早期发现手术并发症可及时干预并降低风险。本研究基于电子健康记录数据（2018-2021年，共89,246例手术，其中49%为男性，中位年龄57岁，四分位距45-69），评估了6种心脏手术后并发症：急性肾损伤、房颤、心脏骤停、深静脉血栓或肺栓塞、输血及术中其他心脏事件。提出一种新型手术变分自编码器（surgVAE），通过跨任务和跨队列表示学习揭示数据内在模式。在五折交叉验证下，与多种主流机器学习及先进表征学习模型对比，surgVAE在所有并发症预测中表现最优，宏平均AUPRC达0.409，宏平均AUROC达0.831，分别比最佳替代方法高出3.4%和3.7%。集成梯度分析揭示了术前关键风险因素。该模型在处理数据复杂性、小样本队列和低频阳性事件方面表现优异，具备强判别力与可解释性，支持数据驱动的患者风险预测与预后评估。
原文摘要 · Abstract (English)
Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning. This retrospective cohort study used data from the electronic health records of adult surgical patients over four years (2018 - 2021). Six key postoperative complications for cardiac surgery were assessed: acute kidney injury, atrial fibrillation, cardiac arrest, deep vein thrombosis or pulmonary embolism, blood transfusion, and other intraoperative cardiac events. We compared prediction performances of surgVAE against widely-used ML models and advanced representation learning and generative models under 5-fold cross-validation. 89,246 surgeries (49% male, median (IQR) age: 57 (45-69)) were included, with 6,502 in the targeted cardiac surgery cohort (61% male, median (IQR) age: 60 (53-70)). surgVAE demonstrated superior performance over existing ML solutions across all postoperative complications of cardiac surgery patients, achieving macro-averaged AUPRC of 0.409 and macro-averaged AUROC of 0.831, which were 3.4% and 3.7% higher, respectively, than the best alternative method (by AUPRC scores). Model interpretation using Integrated Gradients highlighted key risk factors based on preoperative variable importance. surgVAE showed excellent discriminatory performance for predicting postoperative complications and addressing the challenges of data complexity, small cohort sizes, and low-frequency positive events. surgVAE enables data-driven predictions of patient risks and prognosis while enhancing the interpretability of patient risk profiles.
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