  Schizophrenia is a severe mental health condition that requires a long and
complicated diagnostic process. However, early diagnosis is vital to control
symptoms. Deep learning has recently become a popular way to analyse and
interpret medical data. Past attempts to use deep learning for schizophrenia
diagnosis from brain-imaging data have shown promise but suffer from a large
training-application gap - it is difficult to apply lab research to the real
world. We propose to reduce this training-application gap by focusing on
readily accessible data. We collect a data set of psychiatric observations of
patients based on DSM-5 criteria. Because similar data is already recorded in
all mental health clinics that diagnose schizophrenia using DSM-5, our method
could be easily integrated into current processes as a tool to assist
clinicians, whilst abiding by formal diagnostic criteria. To facilitate
real-world usage of our system, we show that it is interpretable and robust.
Understanding how a machine learning tool reaches its diagnosis is essential to
allow clinicians to trust that diagnosis. To interpret the framework, we fuse
two complementary attention mechanisms, 'squeeze and excitation' and
'self-attention', to determine global attribute importance and attribute
interactivity, respectively. The model uses these importance scores to make
decisions. This allows clinicians to understand how a diagnosis was reached,
improving trust in the model. Because machine learning models often struggle to
generalise to data from different sources, we perform experiments with
augmented test data to evaluate the model's applicability to the real world. We
find that our model is more robust to perturbations, and should therefore
perform better in a clinical setting. It achieves 98% accuracy with 10-fold
cross-validation.
