A Reliable Clinical Decision Support System for Posttraumatic Stress Disorder Using Functional Magnetic Resonance Imaging Data

In recent years, there has been an upsurge in artificial intelligence (AI) systems. These systems, along with efficient performance and predictability, also need to incorporate the power of explainability and interpretability. This can significantly aid clinical decision support by providing explainable predictions to assist clinicians. Explainability generally involves uncovering key input features important for classification. However, characterizing the uncertainty underlying the decisions of the AI system is an important aspect needed for interpreting the decisions. This is especially important in clinical decision support systems, given considerations of medical ethics such as nonmaleficence and beneficence. In this study, we develop methods for characterizing the decision certainty of machine learning (ML)-based clinical decision support systems. As an illustrative example, we introduce a framework for ML-based posttraumatic stress disorder (PTSD) diagnostic classification that classifies the subjects into pure and mixed classes. Accordingly, a clinician can have very high confidence (
$\geq$
95% probability) about the diagnosis of a subject in a pure PTSD or combat control class. Remaining sample points for which the AI classification tool does not have very high confidence (
$<$
95% probability) are grouped into a mixed class. Such a scheme will address ethical considerations of nonmaleficence and beneficence since the clinicians can use the AI system to identify those subjects whose diagnosis has a very high degree of confidence (and proceed with treatment accordingly), and refer those in the uncertain/mixed group to further tests. This is a novel approach, in contrast to the existing framework which aims to maximize classification.