Electrical and Computer Engineering Department, American University of Beirut, Beirut, Lebanon.
Pathfinding, Automation Technology and Analytics, Intel Corporation, Hillsboro, Oregon, USA.
BMC Med Inform Decis Mak. 2022 Feb 11;22(1):38. doi: 10.1186/s12911-022-01772-2.
In the last decade, a lot of attention has been given to develop artificial intelligence (AI) solutions for mental health using machine learning. To build trust in AI applications, it is crucial for AI systems to provide for practitioners and patients the reasons behind the AI decisions. This is referred to as Explainable AI. While there has been significant progress in developing stress prediction models, little work has been done to develop explainable AI for mental health.
In this work, we address this gap by designing an explanatory AI report for stress prediction from wearable sensors. Because medical practitioners and patients are likely to be familiar with blood test reports, we modeled the look and feel of the explanatory AI on those of a standard blood test report. The report includes stress prediction and the physiological signals related to stressful episodes. In addition to the new design for explaining AI in mental health, the work includes the following contributions: Methods to automatically generate different components of the report, an approach for evaluating and validating the accuracies of the explanations, and a collection of ground truth of relationships between physiological measurements and stress prediction.
Test results showed that the explanations were consistent with ground truth. The reference intervals for stress versus non-stress were quite distinctive with little variation. In addition to the quantitative evaluations, a qualitative survey, conducted by three expert psychiatrists confirmed the usefulness of the explanation report in understanding the different aspects of the AI system.
In this work, we have provided a new design for explainable AI used in stress prediction based on physiological measurements. Based on the report, users and medical practitioners can determine what biological features have the most impact on the prediction of stress in addition to any health-related abnormalities. The effectiveness of the explainable AI report was evaluated using a quantitative and a qualitative assessment. The stress prediction accuracy was shown to be comparable to state-of-the-art. The contributions of each physiological signal to the stress prediction was shown to correlate with ground truth. In addition to these quantitative evaluations, a qualitative survey with psychiatrists confirmed the confidence and effectiveness of the explanation report in the stress made by the AI system. Future work includes the addition of more explanatory features related to other emotional states of the patient, such as sadness, relaxation, anxiousness, or happiness.
在过去的十年中，人们非常关注使用机器学习为心理健康开发人工智能 (AI) 解决方案。为了建立对 AI 应用程序的信任，至关重要的是，AI 系统要为从业者和患者提供 AI 决策背后的原因。这被称为可解释 AI。虽然在开发压力预测模型方面已经取得了重大进展，但在开发心理健康方面的可解释 AI 方面所做的工作却很少。
在这项工作中，我们通过设计用于从可穿戴传感器预测压力的可解释 AI 报告来解决这一差距。由于医疗从业者和患者可能熟悉血液检测报告，因此我们将可解释 AI 的外观和感觉建模为标准血液检测报告。该报告包括压力预测和与压力事件相关的生理信号。除了在心理健康方面解释 AI 的新设计外，这项工作还包括以下贡献：自动生成报告不同部分的方法、评估和验证解释准确性的方法以及收集生理测量与压力预测之间关系的真实数据。
测试结果表明，解释与真实数据相符。压力与非压力的参考区间非常明显，变化很小。除了定量评估外，三位专家精神病医生进行的定性调查还证实了解释报告在理解 AI 系统不同方面的有用性。
在这项工作中，我们提供了一种基于生理测量的用于压力预测的可解释 AI 的新设计。基于该报告，用户和医疗从业者可以确定哪些生物特征对压力预测的影响最大，除了任何与健康相关的异常之外。通过定量和定性评估来评估可解释 AI 报告的有效性。压力预测准确性与最先进的技术相当。每个生理信号对压力预测的贡献与真实数据相关。除了这些定量评估外，精神病医生的定性调查还证实了 AI 系统产生的压力解释报告的信心和有效性。未来的工作包括添加与患者其他情绪状态（如悲伤、放松、焦虑或快乐）相关的更多解释性特征。
