  In this work we addressed the problem of capturing sequential information
contained in longitudinal electronic health records (EHRs). Clinical notes,
which is a particular type of EHR data, are a rich source of information and
practitioners often develop clever solutions how to maximise the sequential
information contained in free-texts. We proposed a systematic methodology for
learning from chronological events available in clinical notes. The proposed
methodological {\it path signature} framework creates a non-parametric
hierarchical representation of sequential events of any type and can be used as
features for downstream statistical learning tasks. The methodology was
developed and externally validated using the largest in the UK secondary care
mental health EHR data on a specific task of predicting survival risk of
patients diagnosed with Alzheimer's disease. The signature-based model was
compared to a common survival random forest model. Our results showed a
15.4$\%$ increase of risk prediction AUC at the time point of 20 months after
the first admission to a specialist memory clinic and the signature method
outperformed the baseline mixed-effects model by 13.2 $\%$.
