Understanding the research landscape of major depressive disorder via literature mining: an entity-level analysis of PubMed data from 1948 to 2017

Investigating diseases and chemicals in COVID-19 literature with text mining

Given the rapidly unfolding nature of the COVID-19 pandemic, there is an urgent need to streamline the literature synthesis of the growing scientific research to elucidate targeted solutions. Traditional systematic literature review studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, and lack of data-driven tools. This research has collected 9298 papers representing COVID-19 research published through May 5, 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling that provided 25 categories showing associations between the two overarching categories. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.

Exploring diseases and syndromes in neurology case reports from 1955 to 2017 with text mining

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 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.