  In medicine, a communicating virtual patient or doctor allows students to
train in medical diagnosis and develop skills to conduct a medical
consultation. In this paper, we describe a conversational virtual standardized
patient system to allow medical students to simulate a diagnosis strategy of an
abdominal surgical emergency. We exploited the semantic properties captured by
distributed word representations to search for similar questions in the virtual
patient dialogue system. We created two dialogue systems that were evaluated on
datasets collected during tests with students. The first system based on
hand-crafted rules obtains $92.29\%$ as $F1$-score on the studied clinical case
while the second system that combines rules and semantic similarity achieves
$94.88\%$. It represents an error reduction of $9.70\%$ as compared to the
rules-only-based system.
