  We applied machine learning to the unmet medical need of rapid and accurate
diagnosis and prognosis of acute infections and sepsis in emergency
departments. Our solution consists of a Myrna (TM) Instrument and embedded
TriVerity (TM) classifiers. The instrument measures abundances of 29 messenger
RNAs in patient's blood, subsequently used as features for machine learning.
The classifiers convert the input features to an intuitive test report
comprising the separate likelihoods of (1) a bacterial infection (2) a viral
infection, and (3) severity (need for Intensive Care Unit-level care). In
internal validation, the system achieved AUROC = 0.83 on the three-class
disease diagnosis (bacterial, viral, or non-infected) and AUROC = 0.77 on
binary prognosis of disease severity. The Myrna, TriVerity system was granted
breakthrough device designation by the United States Food and Drug
Administration (FDA). This engineering manuscript teaches the standard and
novel machine learning methods used to translate an academic research concept
to a clinical product aimed at improving patient care, and discusses lessons
learned.
