  Background: A large number of neurology case reports have been published, but
it is a challenging task for human medical experts to explore all of these
publications. Text mining offers a computational approach to investigate
neurology literature and capture meaningful patterns. The overarching goal of
this study is to provide a new perspective on case reports of neurological
disease and syndrome analysis over the last six decades using text mining.
  Methods: We extracted diseases and syndromes (DsSs) from more than 65,000
neurology case reports from 66 journals in PubMed over the last six decades
from 1955 to 2017. Text mining was applied to reports on the detected DsSs to
investigate high-frequency DsSs, categorize them, and explore the linear trends
over the 63-year time frame.
  Results: The text mining methods explored high-frequency neurologic DsSs and
their trends and the relationships between them from 1955 to 2017. We detected
more than 18,000 unique DsSs and found 10 categories of neurologic DsSs. While
the trend analysis showed the increasing trends in the case reports for top-10
high-frequency DsSs, the categories had mixed trends.
  Conclusion: Our study provided new insights into the application of text
mining methods to investigate DsSs in a large number of medical case reports
that occur over several decades. The proposed approach can be used to provide a
macro level analysis of medical literature by discovering interesting patterns
and tracking them over several years to help physicians explore these case
reports more efficiently.
