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Integrating genomics and physiologic (high-frequency) data to enhance early sepsis identification

Improved risk stratification and prognosis in sepsis is a critical unmet need. Recent researches on developing predictive models for sepsis prognosis prediction suggest that major challenge to accuracy is due to substantial patient heterogeneity in the sepsis syndrome and a lack of tools to accurately categorize sepsis at the molecular level.

The iGPSD is a brainchild of the researcher from CBMI@UTHSC trying to deploy transcription-based modeling across the sepsis predictive model trained by high-frequency Physiologic data to improve prognostic accuracy.