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Current machine learning models aiming to predict sepsis from ElectronicHealth Records (EHR) do not account for the heterogeneity of the condition,despite its emerging importance in prognosis and treatment. This workdemonstrates the added value of stratifying the types of organ dysfunctionobserved in patients who develop sepsis in the ICU in improving the ability torecognise patients at risk of sepsis from their EHR data. Using an ICU datasetof 13,728 records, we identify clinically significant sepsis subpopulationswith distinct organ dysfunction patterns. Classification experiments usingRandom Forest, Gradient Boost Trees and Support Vector Machines, aiming todistinguish patients who develop sepsis in the ICU from those who do not, showthat features selected using sepsis subpopulations as background knowledgeyield a superior performance regardless of the classification model used. Ourfindings can steer machine learning efforts towards more personalised modelsfor complex conditions including sepsis.

On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning