  Every day, poison control centers (PCC) are called for immediate
classification and treatment recommendations if an acute intoxication is
suspected. Due to the time-sensitive nature of these cases, doctors are
required to propose a correct diagnosis and intervention within a minimal time
frame. Usually the toxin is known and recommendations can be made accordingly.
However, in challenging cases only symptoms are mentioned and doctors have to
rely on their clinical experience. Medical experts and our analyses of a
regional dataset of intoxication records provide evidence that this is
challenging, since occurring symptoms may not always match the textbook
description due to regional distinctions, inter-rater variance, and
institutional workflow. Computer-aided diagnosis (CADx) can provide decision
support, but approaches so far do not consider additional information of the
reported cases like age or gender, despite their potential value towards a
correct diagnosis. In this work, we propose a new machine learning based CADx
method which fuses symptoms and meta information of the patients using graph
convolutional networks. We further propose a novel symptom matching method that
allows the effective incorporation of prior knowledge into the learning process
and evidently stabilizes the poison prediction. We validate our method against
10 medical doctors with different experience diagnosing intoxication cases for
10 different toxins from the PCC in Munich and show our method's superiority in
performance for poison prediction.
