Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multilevel Stochastic Optimization for Imputation in Massive Medical
  Data Records   Exploration and analysis of massive datasets has recently generated
increasing interest in the research and development communities. It has long
been a recognized problem that many datasets contain significant levels of
missing numerical data. We introduce a mathematically principled stochastic
optimization imputation method based on the theory of Kriging. This is shown to
be a powerful method for imputation. However, its computational effort and
potential numerical instabilities produce costly and/or unreliable predictions,
potentially limiting its use on large scale datasets. In this paper, we apply a
recently developed multi-level stochastic optimization approach to the problem
of imputation in massive medical records. The approach is based on
computational applied mathematics techniques and is highly accurate. In
particular, for the Best Linear Unbiased Predictor (BLUP) this multi-level
formulation is exact, and is also significantly faster and more numerically
stable. This permits practical application of Kriging methods to data
imputation problems for massive datasets. We test this approach on data from
the National Inpatient Sample (NIS) data records, Healthcare Cost and
Utilization Project (HCUP), Agency for Healthcare Research and Quality.
Numerical results show the multi-level method significantly outperforms current
approaches and is numerically robust. In particular, it has superior accuracy
as compared with methods recommended in the recent report from HCUP on the
important problem of missing data, which could lead to sub-optimal and poorly
based funding policy decisions. In comparative benchmark tests it is shown that
the multilevel stochastic method is significantly superior to recommended
methods in the report, including Predictive Mean Matching (PMM) and Predicted
Posterior Distribution (PPD), with up to 75% reductions in error.

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The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol