Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Risk factor identification for incident heart failure using neural
  network distillation and variable selection   Recent evidence shows that deep learning models trained on electronic health
records from millions of patients can deliver substantially more accurate
predictions of risk compared to their statistical counterparts. While this
provides an important opportunity for improving clinical decision-making, the
lack of interpretability is a major barrier to the incorporation of these
black-box models in routine care, limiting their trustworthiness and preventing
further hypothesis-testing investigations. In this study, we propose two
methods, namely, model distillation and variable selection, to untangle hidden
patterns learned by an established deep learning model (BEHRT) for risk
association identification. Due to the clinical importance and diversity of
heart failure as a phenotype, it was used to showcase the merits of the
proposed methods. A cohort with 788,880 (8.3% incident heart failure) patients
was considered for the study. Model distillation identified 598 and 379
diseases that were associated and dissociated with heart failure at the
population level, respectively. While the associations were broadly consistent
with prior knowledge, our method also highlighted several less appreciated
links that are worth further investigation. In addition to these important
population-level insights, we developed an approach to individual-level
interpretation to take account of varying manifestation of heart failure in
clinical practice. This was achieved through variable selection by detecting a
minimal set of encounters that can maximally preserve the accuracy of
prediction for individuals. Our proposed work provides a discovery-enabling
tool to identify risk factors in both population and individual levels from a
data-driven perspective. This helps to generate new hypotheses and guides
further investigations on causal links.

36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas