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Text mining: Generating hypotheses from MEDLINE

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  • Padmini Srinivasan

Abstract

Hypothesis generation, a crucial initial step for making scientific discoveries, relies on prior knowledge, experience, and intuition. Chance connections made between seemingly distinct subareas sometimes turn out to be fruitful. The goal in text mining is to assist in this process by automatically discovering a small set of interesting hypotheses from a suitable text collection. In this report, we present open and closed text mining algorithms that are built within the discovery framework established by Swanson and Smalheiser. Our algorithms represent topics using metadata profiles. When applied to MEDLINE, these are MeSH based profiles. We present experiments that demonstrate the effectiveness of our algorithms. Specifically, our algorithms successfully generate ranked term lists where the key terms representing novel relationships between topics are ranked high.

Suggested Citation

  • Padmini Srinivasan, 2004. "Text mining: Generating hypotheses from MEDLINE," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(5), pages 396-413, March.
    • Handle: RePEc:bla:jamist:v:55:y:2004:i:5:p:396-413
    DOI: 10.1002/asi.10389
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    Cited by:

    1. Ola G. El‐Taliawi & Nihit Goyal & Michael Howlett, 2021. "Holding out the promise of Lasswell's dream: Big data analytics in public policy research and teaching," Review of Policy Research, Policy Studies Organization, vol. 38(6), pages 640-660, November.
    2. Ronald N. Kostoff, 2014. "Literature-related discovery: common factors for Parkinson’s Disease and Crohn’s Disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 623-657, September.
    3. João Guerreiro & Paulo Rita & Duarte Trigueiros, 2016. "A Text Mining-Based Review of Cause-Related Marketing Literature," Journal of Business Ethics, Springer, vol. 139(1), pages 111-128, November.
    4. Jingyang Chen & Qin Liu, 2023. "The Green Consumption Behavior Process Mechanism of New Energy Vehicles Driven by Big Data—From a Metacognitive Perspective," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    5. Andrej Kastrin & Dimitar Hristovski, 2021. "Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1415-1451, February.

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