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Extracting Significant Comorbid Diseases from MeSH Index of PubMed

Publication Type : Book Chapter

Publisher : Springer, US

Source : 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

Url : https://link.springer.com/protocol/10.1007/978-1-0716-2305-3_15

Campus : Chennai

School : School of Computing

Year : 2022

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.

Cite this Research Publication : 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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