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

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.

Optimization of genomic classifiers for clinical deployment: evaluation of Bayesian optimization for identification of predictive models of acute infection and in-hospital mortality

Acute infection, if not rapidly and accurately detected, can lead to sepsis, organ failure and even death. Currently, detection of acute infection as well as assessment of a patient's severity of illness are based on imperfect (and often superficial) measures of patient physiology. Characterization of a patient's immune response by quantifying expression levels of key genes from blood represents a potentially more timely and precise means of accomplishing both tasks. Machine learning methods provide a platform for development of deployment-ready classification models robust to the smaller, more heterogeneous datasets typical of healthcare. Identification of promising classifiers is dependent, in part, on hyperparameter optimization (HO), for which a number of approaches including grid search, random sampling and Bayesian optimization have been shown to be effective. In this analysis, we compare HO approaches for the development of diagnostic classifiers of acute infection and in-hospital mortality from gene expression of 29 diagnostic markers. Our comprehensive analysis of a multi-study patient cohort evaluates HO for three different classifier types and over a range of different optimization settings. Consistent with previous research, we find that Bayesian optimization is more efficient than grid search or random sampling-based methods, identifying promising classifiers with fewer evaluated hyperparameter configurations. However, we also find evidence of a lack of correspondence between internal and external validation performance of selected classifiers that complicates model selection for deployment as well as stymies development of clear-cut, practical guidelines for HO application in healthcare. We highlight the need for additional considerations about patient heterogeneity, dataset partitioning and optimization setup when applying HO methods in the healthcare context.