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AMIA Annu Symp Proc. 2018 Apr 16;2017:1332-1341. eCollection 2017.

Identification of Clinically Meaningful Plasma Transfusion Subgroups Using Unsupervised Random Forest Clustering.

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Mayo Clinic, Rochester, MN.


Statistical techniques such as propensity score matching and instrumental variable are commonly employed to "simulate" randomization and adjust for measured confounders in comparative effectiveness research. Despite such adjustments, the results of these methods apply essentially to an "average" patient. However, as patients show significant heterogeneity in their responses to treatments, this average effect is of limited value. It does not account for individual level variabilities, which can deviate substantially from the population average. To address this critical problem, we present a framework that allows the discovery of clinically meaningful homogeneous subgroups with differential effects of plasma transfusion using unsupervised random forest clustering. Subgroup analysis using two blood transfusion datasets show that considerable variablilities exist between the subgroups and population in both the treatment effect of plasma transfusion on bleeding and mortality and risk factors for these outcomes. These results support the customization of blood transfusion therapy for the individual patient.


Plasma transfusion; bleeding; subgroup analysis.; unsupervised learning


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