Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpretable Segmentation of Medical Free-Text Records Based on Word
  Embeddings   Is it true that patients with similar conditions get similar diagnoses? In
this paper we show NLP methods and a unique corpus of documents to validate
this claim. We (1) introduce a method for representation of medical visits
based on free-text descriptions recorded by doctors, (2) introduce a new method
for clustering of patients' visits and (3) present an~application of the
proposed method on a corpus of 100,000 visits. With the proposed method we
obtained stable and separated segments of visits which were positively
validated against final medical diagnoses. We show how the presented algorithm
may be used to aid doctors during their practice.

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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: math
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat