Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hybrid Feature- and Similarity-Based Models for Prediction and
  Interpretation using Large-Scale Observational Data   Introduction: Large-scale electronic health record(EHR) datasets often
include simple informative features like patient age and complex data like care
history that are not easily represented as individual features. Such complex
data have the potential to both improve the quality of risk assessment and to
enable a better understanding of causal factors leading to those risks. We
propose a hybrid feature- and similarity-based model for supervised learning
that combines feature and kernel learning approaches to take advantage of rich
but heterogeneous observational data sources to create interpretable models for
prediction and for investigation of causal relationships. Methods: The proposed
hybrid model is fit by convex optimization with a sparsity-inducing penalty on
the kernel portion. Feature and kernel coefficients can be fit sequentially or
simultaneously. We compared our models to solely feature- and similarity-based
approaches using synthetic data and using EHR data from a primary health care
organization to predict risk of loneliness or social isolation. We also present
a new strategy for kernel construction that is suited to high-dimensional
indicator-coded EHR data. Results: The hybrid models had comparable or better
predictive performance than the feature- and kernel-based approaches in both
the synthetic and clinical case studies. The inherent interpretability of the
hybrid model is used to explore client characteristics stratified by kernel
coefficient direction in the clinical case study; we use simple examples to
discuss opportunities and cautions of the two hybrid model forms when causal
interpretations are desired. Conclusion: Hybrid feature- and similarity-based
models provide an opportunity to capture complex, high-dimensional data within
an additive model structure that supports improved prediction and
interpretation relative to simple models and opaque complex models.

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The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: eess
4
The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th