Depression and anxiety are the two most prevalent mental health disorders worldwide, impacting the lives of millions of people each year. Current screening methods require individuals to manually complete psychometric questionnaires. In this work we develop a deep learning approach to predict psychometric scores given textual data through the use of psycholinguistics features. Data is collected via a dialogue system, were we develop and incorporate an approach to model empathy. Which aims to allow for appropriate use of these systems in a clinical setting. Following a public evaluation, we demonstrate that our approach to model empathy can out perform a similarly trained non empathic approach. Additionally, we show that our deep learning prediction approach performed well on evaluation data, but has diﬃculty generalizing to experimentally collected data. Limitations and implications as a result of this work are discussed.