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Extracting Significant Comorbid Diseases from MeSH Index of PubMed

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Biomedical Text Mining

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2496))

Abstract

Text mining is an important research area to be explored in terms of understanding disease associations and have an insight in disease comorbidities. The reason for comorbid occurrence in any patient may be genetic or molecular interference from any other processes. Comorbidity and multimorbidity may be technically different, yet still are inseparable in studies. They have overlapping nature of associations and hence can be integrated for a more rational approach. The association rule generally used to determine comorbidity may also be helpful in novel knowledge prediction or may even serve as an important tool of assessment in surgical cases. Another approach of interest may be to utilize biological vocabulary resources like UMLS/MeSH across a patient health information and analyze the interrelationship between different health conditions. The protocol presented here can be utilized for understanding the disease associations and analyze at an extensive level.

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Author information

Author notes

  1. Sharanya Manoharan and Oviya Ramalakshmi Iyyappan contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmacology, Cheran College of Pharmacy, Coimbatore, Tamilnadu, India

    Dheepa Anand

  2. Department of Bioinformatics, Stella Maris College (Autonomous), Chennai, Tamilnadu, India

    Sharanya Manoharan

  3. Department of Sciences, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, Tamilnadu, India

    Oviya Ramalakshmi Iyyappan

  4. Department of Biomedical Engineering, PSG College of Technology, Coimbatore, Tamilnadu, India

    Sadhanha Anand

  5. Regenerative Biology, The Morgridge Institute for Research, Madison, WI, USA

    Kalpana Raja

Authors
  1. Dheepa Anand
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  2. Sharanya Manoharan
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  3. Oviya Ramalakshmi Iyyappan
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  4. Sadhanha Anand
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  5. Kalpana Raja
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Editor information

Editors and Affiliations

  1. Morgridge Institute for Research, University of Wisconsin, Madison, WI, USA

    Kalpana Raja

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Anand, D., Manoharan, S., Iyyappan, O.R., Anand, S., Raja, K. (2022). Extracting Significant Comorbid Diseases from MeSH Index of PubMed. In: Raja, K. (eds) Biomedical Text Mining. Methods in Molecular Biology, vol 2496. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2305-3_15

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  • DOI: https://doi.org/10.1007/978-1-0716-2305-3_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2304-6

  • Online ISBN: 978-1-0716-2305-3

  • eBook Packages: Springer Protocols

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