一种用于手术室临床决策支持的因子广义加性模型
A Factored Generalized Additive Model for Clinical Decision Support in the Operating Room
摘要：
逻辑回归（LR）被广泛用于临床预测，因为它部署简单，易于解释。然而，作为一个线性模型，LR的表达能力有限，往往表现不理想。广义加性模型（GAM）通过输入特征的变换来扩展线性模型，尽管并非所有GAM变体都允许特征交互。在本文中，我们提出了一种因子化广义加性模型（F-GAM），以保留目标特征的模型可解释性，同时允许丰富的模型与个人内部固定的特征进行交互。我们从单中心数据库中评估F-GAM对术后急性肾损伤和急性呼吸衰竭两个目标的预测。我们发现优越的模型性能的F-GAM的AUPRC和AUROC相比，其他几个GAM实现，随机森林，支持向量机，和一个深神经网络。我们发现，模型的可解释性是好的，结果具有较高的表面效度。
英文摘要：
Logistic regression (LR) is widely used in clinical prediction because it is simple to deploy and easy to interpret. Nevertheless, being a linear model, LR has limited expressive capability and often has unsatisfactory performance. Generalized additive models (GAMs) extend the linear model with transformations of input features, though feature interaction is not allowed for all GAM variants. In this paper, we propose a factored generalized additive model (F-GAM) to preserve the model interpretability for targeted features while allowing a rich model for interaction with features fixed within the individual. We evaluate F-GAM on prediction of two targets, postoperative acute kidney injury and acute respiratory failure, from a single-center database. We find superior model performance of F-GAM in terms of AUPRC and AUROC compared to several other GAM implementations, random forests, support vector machine, and a deep neural network. We find that the model interpretability is good with results with high face validity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
