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A systems approach to understand the ICU Co-morbidome

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dc.contributor.author Kaur, Dilraj
dc.contributor.author Sethi, Tavpritesh (Advisor)
dc.contributor.author Ramachandran, Srinivasan (Advisor)
dc.date.accessioned 2018-02-16T10:53:43Z
dc.date.available 2018-02-16T10:53:43Z
dc.date.issued 2017-11
dc.identifier.uri http://repository.iiitd.edu.in/xmlui/handle/123456789/611
dc.description.abstract Sepsis is one of biggest causes of mortality in patients admitted to the Intensive Care Units across the world. Its incidence in the developing countries including India is expected to be much higher than the developed world where more data are available. Since its incidence and co-morbidities are is increased at the extremes of age we carried out a co-morbidity analysis using a networks approach across on ICU data across different age groups (neonates, infants, children, adults and elderly) on a total 36094 patients admitted and available through the Physionet portal. The thesis illustrates the utility of networks for addressing comorbidities and a comparison of different metrics that were tested for network construction. Multiple association measures such as odds ratio, jaccard, cosine and dice measures were used to get the association. The association networks were then subjected to community detection algorithms for visualizing clusters of comorbidities in ICU. 48 different centrality measures were then computed upon each of the nodes and their distribution was visualized to identify most important features of the ICU co-morbidome and its dynamics across different age groups. Since association networks are dense, sparsity was introduced through a Bayesian network approach and for validation of the key nodes in the ICU co-morbidome en_US
dc.language.iso en_US en_US
dc.subject ICU en_US
dc.subject Bayesian Network en_US
dc.title A systems approach to understand the ICU Co-morbidome en_US
dc.type Thesis en_US

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