2019/05/23 by Amir Karami, Karami, Amir, Mehdi Ghasemi +8
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Biological sciences #FOS: Computer and information sciences #Genomics and Rare Diseases #Information Retrieval (cs.IR) #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.1906.03183
openalex publication_date 2019/05/23 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Background: A large number of neurology case reports have been published, but\nit is a challenging task for human medical experts to explore all of these\npublications. Text mining offers a computational approach to investigate\nneurology literature and capture meaningful patterns. The overarching goal of\nthis study is to provide a new perspective on case reports of neurological\ndisease and syndrome analysis over the last six decades using text mining.\n Methods: We extracted diseases and syndromes (DsSs) from more than 65,000\nneurology case reports from 66 journals in PubMed over the last six decades\nfrom 1955 to 2017. Text mining was applied to reports on the detected DsSs to\ninvestigate high-frequency DsSs, categorize them, and explore the linear trends\nover the 63-year time frame.\n Results: The text mining methods explored high-frequency neurologic DsSs and\ntheir trends and the relationships between them from 1955 to 2017. We detected\nmore than 18,000 unique DsSs and found 10 categories of neurologic DsSs. While\nthe trend analysis showed the increasing trends in the case reports for top-10\nhigh-frequency DsSs, the categories had mixed trends.\n Conclusion: Our study provided new insights into the application of text\nmining methods to investigate DsSs in a large number of medical case reports\nthat occur over several decades. The proposed approach can be used to provide a\nmacro level analysis of medical literature by discovering interesting patterns\nand tracking them over several years to help physicians explore these case\nreports more efficiently.\n