Comparison of comorbidities across the three cohorts. Similarities were determined by comparing the best-matched clusters identified by both algorithms. The table presents the degree and betweenness values for each comorbidity within the cohorts.

<p>Comparison of comorbidities across the three cohorts. Similarities were determined by comparing the best-matched clusters identified by both algorithms. The table presents the degree and betweenness values for each comorbidity within the cohorts.</p>

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Bibliographic Details
Main Author:	Mohammad A. Al-Mamun (6086642) (author)
Other Authors:	Ki Jin Jeun (17374655) (author), Todd Brothers (9383541) (author), Ernest O. Asare (12372721) (author), Khaled Shawwa (2586589) (author), Imtiaz Ahmed (574504) (author)
Published:	2025
Subjects:	Medicine Microbiology Cell Biology Developmental Biology Marine Biology Cancer Mathematical Sciences not elsewhere classified patient profiling algorithm ischemic heart disease including high cholesterol identify patient profiles identify clinical phenotypes gi tract components variable clustering methods top three clusters presented using comorbidities hospital acquired aki 876 ckd patients 37 ), osteoarthritis two methods three groups average comorbidities aki patients aki ), 14 ), xlink "> urgent need several conditions promising approach onset ckd mean number lactic acidosis higher number higher degree find similarities edges connected comorbidity network combining network classified aki ckd ). betweenness centrality based community aki groups aki group aki cohort 84 vs
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Comparison of comorbidities across the three cohorts. Similarities were determined by comparing the best-matched clusters identified by both algorithms. The table presents the degree and betweenness values for each comorbidity within the cohorts.

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