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首页> 外文期刊>BMC Medical Informatics and Decision Making >Interpretable clinical prediction via attention-based neural network
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Interpretable clinical prediction via attention-based neural network

机译：基于关注的神经网络解释的临床预测

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摘要

The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in the last decade, which accumulated abundant electronic patient data, neural networks or deep learning techniques are gradually being applied to clinical tasks by utilizing the huge potential of EHR data. However, typical deep learning models are black-boxes, which are not transparent and the prediction outcomes of which are difficult to interpret. To remedy this limitation, we propose an attention neural network model for interpretable clinical prediction. In detail, the proposed model employs an attention mechanism to capture critical/essential features with their attention signals on the prediction results, such that the predictions generated by the neural network model can be interpretable. We evaluate our proposed model on a real-world clinical dataset consisting of 736 samples to predict readmissions for heart failure patients. The performance of the proposed model achieved 66.7 and 69.1% in terms of accuracy and AUC, respectively, and outperformed the baseline models. Besides, we displayed patient-specific attention weights, which can not only help clinicians understand the prediction outcomes, but also assist them to select individualized treatment strategies or intervention plans. The experimental results demonstrate that the proposed model can improve both the prediction performance and interpretability by equipping the model with an attention mechanism.
机译：机器学习模型预测的结果的可解释性是至关重要的，特别是在医疗保健等关键领域。随着过去十年的医疗组织越来越多的电子医疗组织（EHR），积累了丰富的电子患者数据，神经网络或深度学习技术通过利用EHR数据的巨大潜力逐渐应用于临床任务。然而，典型的深度学习模型是黑匣子，这是不透明的，并且难以解释的预测结果。为了解决这个限制，我们提出了一种注意神经网络模型，可解释临床预测。详细地，所提出的模型采用注意机制来捕获临界/基本特征，其注意信号上的预测结果，使得由神经网络模型产生的预测可以是可解释的。我们在由736个样本组成的现实世界临床数据集中评估我们提出的模型，以预测心力衰竭患者的阅。拟议模型的性能分别在准确性和AUC方面实现了66.7和69.1％，并且优于基线模型。此外，我们展示了特定的患者的注意力，这不仅可以帮助临床医生了解预测结果，还可以帮助他们选择个性化的治疗策略或干预计划。实验结果表明，该模型可以通过用注意机制装配模型来改善预测性能和解释性。

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