Artificial Intelligence techniques can be used to classify a patient's physical activities and predict vital signs for remote patient monitoring. Regression analysis based on non-linear models like deep learning models has limited explainability due to its black-box nature. This can require decision-makers to make blind leaps of faith based on non-linear model results, especially in healthcare applications. In non-invasive monitoring, patient data from tracking sensors and their predisposing clinical attributes act as input features for predicting future vital signs. Explaining the contributions of various features to the overall output of the monitoring application is critical for a clinician's decision-making. In this study, an Explainable AI for Quantitative analysis (QXAI) framework is proposed with post-hoc model explainability and intrinsic explainability for regression and classification tasks in a supervised learning approach. This was achieved by utilizing the Shapley values concept and incorporating attention mechanisms in deep learning models. We adopted the artificial neural networks (ANN) and attention-based Bidirectional LSTM (BiLSTM) models for the prediction of heart rate and classification of physical activities based on sensor data. The deep learning models achieved state-of-the-art results in both prediction and classification tasks. Global explanation and local explanation were conducted on input data to understand the feature contribution of various patient data. The proposed QXAI framework was evaluated using PPG-DaLiA data to predict heart rate and mobile health (MHEALTH) data to classify physical activities based on sensor data. Monte Carlo approximation was applied to the framework to overcome the time complexity and high computation power requirements required for Shapley value calculations.

翻译：人工智能技术可用于分类患者的身体活动并预测远程患者监护的生命体征。基于深度学习模型等非线性模型的回归分析因其黑箱特性而可解释性有限。这可能导致决策者基于非线性模型结果做出盲目的信任飞跃，尤其是在医疗保健应用中。在非侵入式监护中，来自跟踪传感器的患者数据及其易感临床特征作为输入特征用于预测未来生命体征。解释各种特征对监护应用整体输出的贡献对于临床医生的决策至关重要。本研究提出了一种面向定量分析的可解释人工智能（QXAI）框架，该框架结合了事后模型可解释性和内在可解释性，用于监督学习方法中的回归和分类任务。通过利用沙普利值概念并在深度学习模型中引入注意力机制实现了这一点。我们采用人工神经网络（ANN）和基于注意力的双向长短期记忆网络（BiLSTM）模型，基于传感器数据预测心率和分类身体活动。深度学习模型在预测和分类任务中均取得了最先进的结果。对输入数据进行了全局解释和局部解释，以理解各种患者数据的特征贡献。使用PPG-DaLiA数据预测心率，以及使用移动健康（MHEALTH）数据基于传感器数据分类身体活动，对所提出的QXAI框架进行了评估。采用蒙特卡洛近似法应用于该框架，以克服沙普利值计算所需的时间复杂性和高计算能力需求。