Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SAVEHR: Self Attention Vector Representations for EHR based Personalized
  Chronic Disease Onset Prediction and Interpretability   Chronic disease progression is emerging as an important area of investment
for healthcare providers. As the quantity and richness of available clinical
data continue to increase along with advances in machine learning, there is
great potential to advance our approaches to caring for patients. An ideal
approach to this problem should generate good performance on at least three
axes namely, a) perform across many clinical conditions without requiring deep
clinical expertise or extensive data scientist effort, b) generalization across
populations, and c) be explainable (model interpretability). We present SAVEHR,
a self-attention based architecture on heterogeneous structured EHR data that
achieves $>$ 0.51 AUC-PR and $>$ 0.87 AUC-ROC gains on predicting the onset of
four clinical conditions (CHF, Kidney Failure, Diabetes and COPD) 15-months in
advance, and transfers with high performance onto a new population. We
demonstrate that SAVEHR model performs superior to ten baselines on all three
axes stated formerly.

14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg