Medically-oriented design for explainable AI for stress prediction from physiological measurements

Abstract

Background 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. Methods 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. Results 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. Conclusion 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.

Using Explainable AI to understand frailty indicators

“…Frailty research has extensively utilized machine learning [25][26][27][28][29] and explainable AI [38] That can shed light on the mechanisms of diseases and identify biomarkers that can be used to diagnose or find a treatment for a specific medical condition. Moreover, it can be extended to other applications, such as understanding the prognostic factors or severity modulators [39].…”

Evaluation of stress based on multiple distinct modalities using machine learning techniques

“…Jaber et al [24] aimed to improve the user-friendliness and practicality of AI-based stress prediction by creating an explanatory report modeled after standard blood test reports. The report presents a stress prediction model that incorporates physiological signals, which can aid patients and practitioners in comprehending the rationale behind the prediction.…”

Artificial Intelligence‐Based Medical Sensors for Healthcare System

“…Reproduced with permission. [108] Copyright 2022, BioMed Central. temperature changes by physical means.…”

“…Jaber et al developed an AI system design to explain mental health on the basis of the physiological data recorded by wearable devices (Figure 4C). [108] First, the shapley additive explanations (SHAP) model is used to calculate contribution of each physiological factor to the overall stress probability. Then, the balanced random forest classifier was chosen to predict stress because of the reliable results and strong ability to handle unbalanced datasets.…”

“…C) Schematic of a new design for explainable AI used in stress prediction based on physiological measurements. Reproduced with permission [108]. Copyright 2022, BioMed Central.…”