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

Exploring Diseases and Syndromes in Neurology Case Reports from 1955 to 2017 with Text Mining