Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining chest X-rays and electronic health record (EHR) data using
  machine learning to diagnose acute respiratory failure   Objective: When patients develop acute respiratory failure, accurately
identifying the underlying etiology is essential for determining the best
treatment. However, differentiating between common medical diagnoses can be
challenging in clinical practice. Machine learning models could improve medical
diagnosis by aiding in the diagnostic evaluation of these patients. Materials
and Methods: Machine learning models were trained to predict the common causes
of acute respiratory failure (pneumonia, heart failure, and/or COPD). Models
were trained using chest radiographs and clinical data from the electronic
health record (EHR) and applied to an internal and external cohort. Results:
The internal cohort of 1,618 patients included 508 (31%) with pneumonia, 363
(22%) with heart failure, and 137 (8%) with COPD based on physician chart
review. A model combining chest radiographs and EHR data outperformed models
based on each modality alone. Models had similar or better performance compared
to a randomly selected physician reviewer. For pneumonia, the combined model
area under the receiver operating characteristic curve (AUROC) was 0.79
(0.77-0.79), image model AUROC was 0.74 (0.72-0.75), and EHR model AUROC was
0.74 (0.70-0.76). For heart failure, combined: 0.83 (0.77-0.84), image: 0.80
(0.71-0.81), and EHR: 0.79 (0.75-0.82). For COPD, combined: AUROC = 0.88
(0.83-0.91), image: 0.83 (0.77-0.89), and EHR: 0.80 (0.76-0.84). In the
external cohort, performance was consistent for heart failure and increased for
COPD, but declined slightly for pneumonia. Conclusions: Machine learning models
combining chest radiographs and EHR data can accurately differentiate between
common causes of acute respiratory failure. Further work is needed to determine
how these models could act as a diagnostic aid to clinicians in clinical
settings.

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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: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: stat
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The categories of this paper are: nlin
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The categories of this paper are: astro-ph