  The development of decision support systems is a growing domain that can be
applied in the area of disease control and diagnostics. Using video-based
surveillance data, skeleton features are extracted to perform action
recognition, specifically the detection and recognition of coughing and
sneezing motions. Providing evidence of flu-like symptoms, a decision support
system based on causal networks is capable of providing the operator with vital
information for decision-making. A modified residual temporal convolutional
network is proposed for action recognition using skeleton features. This paper
addresses the capability of using results from a machine-learning model as
evidence for a cognitive decision support system. We propose risk and trust
measures as a metric to bridge between machine-learning and machine-reasoning.
We provide experiments on evaluating the performance of the proposed network
and how these performance measures can be combined with risk to generate trust.
