  The availability of mobile technologies has enabled the efficient collection
prospective longitudinal, ecologically valid self-reported mood data from
psychiatric patients. These data streams have potential for improving the
efficiency and accuracy of psychiatric diagnosis as well predicting future mood
states enabling earlier intervention. However, missing responses are common in
such datasets and there is little consensus as to how this should be dealt with
in practice. A signature-based method was used to capture different elements of
self-reported mood alongside missing data to both classify diagnostic group and
predict future mood in patients with bipolar disorder, borderline personality
disorder and healthy controls. The missing-response-incorporated
signature-based method achieves roughly 66\% correct diagnosis, with f1 scores
for three different clinic groups 59\% (bipolar disorder), 75\% (healthy
control) and 61\% (borderline personality disorder) respectively. This was
significantly more efficient than the naive model which excluded missing data.
Accuracies of predicting subsequent mood states and scores were also improved
by inclusion of missing responses. The signature method provided an effective
approach to the analysis of prospectively collected mood data where missing
data was common and should be considered as an approach in other similar
datasets.
