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身体活动并预测生命体征，以实现远程患者监护。基于深度学习模型等非线性模型的回归分析，因其黑箱特性而存在有限的可解释性。这可能导致决策者（尤其是在医疗健康应用中）依据非线性模型结果做出盲目的主观判断。在无创监护中，来自跟踪传感器的患者数据及其倾向性临床属性作为输入特征，用于预测未来生命体征。解释不同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，通过监督学习方法实现了回归与分类任务的后验模型可解释性与内在可解释性。这通过利用Shapley值概念并在深度学习模型中引入注意力机制实现。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，基于传感器数据进行心率预测与身体活动分类。深度学习模型在预测和分类任务中均取得了最优结果。对输入数据进行全局解释与局部解释，以理解不同患者数据的特征贡献。所提出的QXAI框架利用PPG-DaLiA数据评估心率预测性能，并利用移动健康（MHEALTH）数据评估基于传感器数据的身体活动分类性能。该框架采用蒙特卡洛近似方法，以克服Shapley值计算所需的时间复杂度与高计算资源需求。