Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis

TL;DR

This paper presents a machine learning system using blood RNA measurements to rapidly diagnose and assess severity of infections and sepsis, aiming to improve emergency care.

Contribution

It introduces a novel blood-based diagnostic and prognostic system with embedded classifiers, validated with high accuracy and granted FDA breakthrough device status.

Findings

Achieved AUROC = 0.83 for disease diagnosis

Achieved AUROC = 0.77 for severity prognosis

System received FDA breakthrough device designation

Abstract

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.