In this work, we propose a novel diagnostic workflow-DigEST-that will enable stratification of disease states based on severity using multiplexed point of care (POC) biosensors. This work can boost the performance of current POC tests by enabling clear, digestible, and actionable diagnoses to the end user. The scheme can be applied to any disease model, which requires time-critical disease stratification for personalized treatment. Here, urinary tract infection is explored as the proof-ofconcept disease model and a four-class classification of disease severity is discussed.Our method is superior to traditional enzyme-linked immunosorbent assay (ELISA) as it is faster and can work with multiple disease biomarkers and categorize diseases by endotypes (or disease subtype) and severity. To map the nonlinear nature of biochemical pathways of complex diseases, the method utilizes an established supervised machine learning model for digital classification. This scheme can potentially boost the diagnostic power of current electrochemical biosensors for better precision therapy and improved patient outcomes.

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community sizes were observed in Stage 1 cystitis bladder biopsies (8.4 per 10x1 μm 2 , 95% C.I. 6.6 e 10.1). CONCLUSIONS: For the first time, 16s rRNA FISH detected Escherichia spp. in the bladders of postmenopausal women electing to undergo EF for the advanced management of rUTI. Interestingly, bladder-resident bacterial community sizes were highest in bladder biopsies from women with Stage 1 cystitis.