Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Psoriasis Severity Assessment with a Similarity-Clustering Machine
  Learning Approach Reduces Intra- and Inter-observation variation   Psoriasis is a complex disease with many variations in genotype and
phenotype. General advancements in medicine has further complicated both
assessments and treatment for both physicians and dermatologist alike. Even
with all of our technological progress we still primarily use the assessment
tool Psoriasis Area and Severity Index (PASI) for severity assessments which
was developed in the 1970s. In this study we evaluate a method involving
digital images, a comparison web application and similarity clustering,
developed to improve the assessment tool in terms of intra- and inter-observer
variation. Images of patients was collected from a mobile device. Images were
captured of the same lesion area taken approximately 1 week apart. Five
dermatologists evaluated the severity of psoriasis by modified-PASI, absolute
scoring and a relative pairwise PASI scoring using similarity-clustering and
conducted using a web-program displaying two images at a time. mPASI scoring of
single photos by the same or different dermatologist showed mPASI ratings of
50% to 80%, respectively. Repeated mPASI comparison using similarity clustering
showed consistent mPASI ratings > 95%. Pearson correlation between absolute
scoring and pairwise scoring progression was 0.72.

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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con