Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Building Deep Learning Models to Predict Mortality in ICU Patients   Mortality prediction in intensive care units is considered one of the
critical steps for efficiently treating patients in serious condition. As a
result, various prediction models have been developed to address this problem
based on modern electronic healthcare records. However, it becomes increasingly
challenging to model such tasks as time series variables because some
laboratory test results such as heart rate and blood pressure are sampled with
inconsistent time frequencies. In this paper, we propose several deep learning
models using the same features as the SAPS II score. To derive insight into the
proposed model performance. Several experiments have been conducted based on
the well known clinical dataset Medical Information Mart for Intensive Care
III. The prediction results demonstrate the proposed model's capability in
terms of precision, recall, F1 score, and area under the receiver operating
characteristic curve.

21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph