Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Graph-based Imputation Method for Sparse Medical Records   Electronic Medical Records (EHR) are extremely sparse. Only a small
proportion of events (symptoms, diagnoses, and treatments) are observed in the
lifetime of an individual. The high degree of missingness of EHR can be
attributed to a large number of factors, including device failure, privacy
concerns, or other unexpected reasons. Unfortunately, many traditional
imputation methods are not well suited for highly sparse data and scale poorly
to high dimensional datasets. In this paper, we propose a graph-based
imputation method that is both robust to sparsity and to unreliable unmeasured
events. Our approach compares favourably to several standard and
state-of-the-art imputation methods in terms of performance and runtime.
Moreover, results indicate that the model learns to embed different event types
in a clinically meaningful way. Our work can facilitate the diagnosis of novel
diseases based on the clinical history of past events, with the potential to
increase our understanding of the landscape of comorbidities.

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The categories of this paper are: astro-ph
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: stat