Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Categorical Co-Frequency Analysis: Clustering Diagnosis Codes to Predict
  Hospital Readmissions   Accurately predicting patients' risk of 30-day hospital readmission would
enable hospitals to efficiently allocate resource-intensive interventions. We
develop a new method, Categorical Co-Frequency Analysis (CoFA), for clustering
diagnosis codes from the International Classification of Diseases (ICD)
according to the similarity in relationships between covariates and readmission
risk. CoFA measures the similarity between diagnoses by the frequency with
which two diagnoses are split in the same direction versus split apart in
random forests to predict readmission risk. Applying CoFA to de-identified data
from Berkshire Medical Center, we identified three groups of diagnoses that
vary in readmission risk. To evaluate CoFA, we compared readmission risk models
using ICD majors and CoFA groups to a baseline model without diagnosis
variables. We found substituting ICD majors for the CoFA-identified clusters
simplified the model without compromising the accuracy of predictions. Fitting
separate models for each ICD major and CoFA group did not improve predictions,
suggesting that readmission risk may be more homogeneous that heterogeneous
across diagnosis groups.

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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
2
The categories of this paper are: math
3
The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
1
The categories of this paper are: cs
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The categories of this paper are: mtrl-th