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首页> 外文期刊>BMC Medical Informatics and Decision Making >A non-linear ensemble model-based surgical risk calculator for mixed data from multiple surgical fields
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A non-linear ensemble model-based surgical risk calculator for mixed data from multiple surgical fields

机译：基于非线性集合模型的外科手术风险计算器，用于来自多个外科字段的混合数据

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The misestimation of surgical risk is a serious threat to the lives of patients when implementing surgical risk calculator. Improving the accuracy of postoperative risk prediction has received much attention and many methods have been proposed to cope with this problem in the past decades. However, those linear approaches are inable to capture the non-linear interactions between risk factors, which have been proved to play an important role in the complex physiology of the human body, and thus may attenuate the performance of surgical risk calculators. In this paper, we presented a new surgical risk calculator based on a non-linear ensemble algorithm named Gradient Boosting Decision Tree (GBDT) model, and explored the corresponding pipeline to support it. In order to improve the practicability of our approach, we designed three different modes to deal with different data situations. Meanwhile, considering that one of the obstacles to clinical acceptance of surgical risk calculators was that the model was too complex to be used in practice, we reduced the number of input risk factors according to the importance of them in GBDT. In addition, we also built some baseline models and similar models to compare with our approach. The data we used was three-year clinical data from Surgical Outcome Monitoring and Improvement Program (SOMIP) launched by the Hospital Authority of Hong Kong. In all experiments our approach shows excellent performance, among which the best result of area under curve (AUC), Hosmer–Lemeshow test ( $${{mathrm{HL}}}_{hat{c}}$$ ) and brier score (BS) can reach 0.902, 7.398 and 0.047 respectively. After feature reduction, the best result of AUC, $${mathrm{HL}}_{hat{c}}$$ and BS of our approach can still be maintained at 0.894, 7.638 and 0.060, respectively. In addition, we also performed multiple groups of comparative experiments. The results show that our approach has a stable advantage in each evaluation indicator. The experimental results demonstrate that NL-SRC can not only improve the accuracy of predicting the surgical risk of patients, but also effectively capture important risk factors and their interactions. Meanwhile, it also has excellent performance on the mixed data from multiple surgical fields.
机译：在实施外科风险计算器时，手术风险的折衷是对患者的生命的严重威胁。提高术后风险预测的准确性得到了很多关注，并且已经提出了许多方法在过去几十年中应对这个问题。然而，那些线性方法是可以捕获风险因素之间的非线性相互作用，这被证明已经在人体的复杂生理中发挥着重要作用，因此可以衰减外科风险计算器的性能。在本文中，我们介绍了一种基于名为渐变升压决策树（GBDT）模型的非线性集合算法的新的外科危险计算器，并探索了相应的流水线来支持它。为了提高我们方法的实用性，我们设计了三种不同的模式来处理不同的数据情况。同时，考虑到临床接受外科风险计算器的障碍之一是该模型在实践中过于复杂，我们根据他们在GBDT中的重要性减少了输入风险因素的数量。此外，我们还建立了一些基线模型和类似模型，以与我们的方法进行比较。我们使用的数据是香港医院管理局推出的外科结果监测和改善计划（SOMIP）的三年临床资料。在所有实验中，我们的方法表现出优异的性能，其中曲线（AUC）下的最佳结果（AUC），Hosmer-Lemeshow测试（$$ {{{{ mathrm {hl}}} _ { hat {c} $$） BRIER得分（BS）分别可达0.902,7.398和0.047。在减少特征后，AUC，$$ { MATHRM {HL}} _ { hat {c}} $$和bs的方法仍然可以分别保持在0.894,7.638和0.060。此外，我们还进行了多组比较实验。结果表明，我们的方法在每个评估指标中具有稳定的优势。实验结果表明，NL-SRC不仅可以提高预测患者手术风险的准确性，而且还有效地捕获了重要的风险因素及其互动。同时，它在来自多个外科领域的混合数据中也具有出色的性能。

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