QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems
CoRR(2023)
摘要
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.
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关键词
explainable qxai framework,quantitative analysis,monitoring
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