Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Measuring Pain in Sickle Cell Disease using Clinical Text   Sickle Cell Disease (SCD) is a hereditary disorder of red blood cells in
humans. Complications such as pain, stroke, and organ failure occur in SCD as
malformed, sickled red blood cells passing through small blood vessels get
trapped. Particularly, acute pain is known to be the primary symptom of SCD.
The insidious and subjective nature of SCD pain leads to challenges in pain
assessment among Medical Practitioners (MPs). Thus, accurate identification of
markers of pain in patients with SCD is crucial for pain management.
Classifying clinical notes of patients with SCD based on their pain level
enables MPs to give appropriate treatment. We propose a binary classification
model to predict pain relevance of clinical notes and a multiclass
classification model to predict pain level. While our four binary machine
learning (ML) classifiers are comparable in their performance, Decision Trees
had the best performance for the multiclass classification task achieving 0.70
in F-measure. Our results show the potential clinical text analysis and machine
learning offer to pain management in sickle cell patients.

23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas