Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Critical Review of the state-of-the-art on Deep Neural Networks for
  Blood Glucose Prediction in Patients with Diabetes   This article compares ten recently proposed neural networks and proposes two
ensemble neural network-based models for blood glucose prediction. All of them
are tested under the same dataset, preprocessing workflow, and tools using the
OhioT1DM Dataset at three different prediction horizons: 30, 60, and 120
minutes. We compare their performance using the most common metrics in blood
glucose prediction and rank the best-performing ones using three methods
devised for the statistical comparison of the performance of multiple
algorithms: scmamp, model confidence set, and superior predictive ability. Our
analysis highlights those models with the highest probability of being the best
predictors, estimates the increase in error of the models that perform more
poorly with respect to the best ones, and provides a guide for their use in
clinical practice.

4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg