Shao Yijun, Cheng Yan, Shah Rashmee U, Weir Charlene R, Bray Bruce E, Zeng-Treitler Qing
Biomedical Informatics Center, George Washington University, Washington, DC, USA.
Washington DC VA Medical Center, Washington, DC, USA.
J Med Syst. 2021 Jan 4;45(1):5. doi: 10.1007/s10916-020-01701-8.
Deep neural network models are emerging as an important method in healthcare delivery, following the recent success in other domains such as image recognition. Due to the multiple non-linear inner transformations, deep neural networks are viewed by many as black boxes. For practical use, deep learning models require explanations that are intuitive to clinicians. In this study, we developed a deep neural network model to predict outcomes following major cardiovascular procedures, using temporal image representation of past medical history as input. We created a novel explanation for the prediction of the model by defining impact scores that associate clinical observations with the outcome. For comparison, a logistic regression model was fitted to the same dataset. We compared the impact scores and log odds ratios by calculating three types of correlations, which provided a partial validation of the impact scores. The deep neural network model achieved an area under the receiver operating characteristics curve (AUC) of 0.787, compared to 0.746 for the logistic regression model. Moderate correlations were found between the impact scores and the log odds ratios. Impact scores generated by the explanation algorithm has the potential to shed light on the "black box" deep neural network model and could facilitate its adoption by clinicians.
随着深度学习模型在图像识别等其他领域取得的成功，深度神经网络模型正成为医疗保健领域的一种重要方法。由于存在多个非线性内部变换，许多人将深度神经网络视为黑箱。在实际应用中，深度学习模型需要提供让临床医生易于理解的解释。在本研究中，我们开发了一种深度神经网络模型，以过去病史的时间图像表示作为输入，来预测重大心血管手术后的结果。我们通过定义将临床观察与结果相关联的影响分数，为模型的预测创建了一种新颖的解释。为了进行比较，我们对同一数据集拟合了逻辑回归模型。我们通过计算三种类型的相关性来比较影响分数和对数比值比，这为影响分数提供了部分验证。深度神经网络模型的受试者工作特征曲线下面积（AUC）为0.787，而逻辑回归模型为0.746。在影响分数和对数比值比之间发现了中等程度的相关性。解释算法生成的影响分数有可能揭示“黑箱”深度神经网络模型，并有助于临床医生采用该模型。
