Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An efficient representation of chronological events in medical texts   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 $\%$.

34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th