Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Sequential Models for Suicidal Ideation from Multiple Source Data   This article presents a novel method for predicting suicidal ideation from
Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data
using deep sequential models. Both EHR longitudinal data and EMA question forms
are defined by asynchronous, variable length, randomly-sampled data sequences.
In our method, we model each of them with a Recurrent Neural Network (RNN), and
both sequences are aligned by concatenating the hidden state of each of them
using temporal marks. Furthermore, we incorporate attention schemes to improve
performance in long sequences and time-independent pre-trained schemes to cope
with very short sequences. Using a database of 1023 patients, our experimental
results show that the addition of EMA records boosts the system recall to
predict the suicidal ideation diagnosis from 48.13% obtained exclusively from
EHR-based state-of-the-art methods to 67.78%. Additionally, our method provides
interpretability through the t-SNE representation of the latent space. Further,
the most relevant input features are identified and interpreted medically.

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The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio