- Machine learning (ML) algorithms that leveraged an administrative database outperformed conventional methods of predicting sepsis mortality rates, according to a study published in the Journal of Medical Internet Research.
The Centers for Disease Control and Prevention (CDC) report that 1.7 million adults in the US develop sepsis in a typical year, and 270,000 of them die as a result. Some patients who survive sepsis also develop permanent organ damage and have a higher risk of other medical conditions, even several years after recovering. Costs incurred from sepsis treatment vary based on the severity of the infection and whether the infection was present at the time of admission to the hospital, but research indicates potential costs between $16,324 and $51,022 per patient. Patients who develop other medical conditions resulting from sepsis incur additional costs.
ML models have previously been applied to point-of-care sepsis mortality prediction, but the new study aimed to compare the performance of ML algorithms to that of conventional prediction models for in-hospital sepsis mortality utilizing administrative data.
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