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

Ljubomir Buturovic, Michael Mayhew, Roland Luethy, Kirindi Choi, Uros Midic, Nandita Damaraju, Yehudit Hasin-Brumshtein, Amitesh Pratap, Rhys Adams, Joao Fonseca, Ambika Srinath, Paul Fleming, Claudia Pereira, Oliver Liesenfeld, Purvesh Khatri, Timothy Sweeney
Proceedings of the 4th Machine Learning for Health Symposium, PMLR 259:154-170, 2025.

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 Instrument and embedded TriVerity 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v259-buturovic25a, title = {Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis}, author = {Buturovic, Ljubomir and Mayhew, Michael and Luethy, Roland and Choi, Kirindi and Midic, Uros and Damaraju, Nandita and Hasin-Brumshtein, Yehudit and Pratap, Amitesh and Adams, Rhys and Fonseca, Joao and Srinath, Ambika and Fleming, Paul and Pereira, Claudia and Liesenfeld, Oliver and Khatri, Purvesh and Sweeney, Timothy}, booktitle = {Proceedings of the 4th Machine Learning for Health Symposium}, pages = {154--170}, year = {2025}, editor = {Hegselmann, Stefan and Zhou, Helen and Healey, Elizabeth and Chang, Trenton and Ellington, Caleb and Mhasawade, Vishwali and Tonekaboni, Sana and Argaw, Peniel and Zhang, Haoran}, volume = {259}, series = {Proceedings of Machine Learning Research}, month = {15--16 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v259/main/assets/buturovic25a/buturovic25a.pdf}, url = {https://proceedings.mlr.press/v259/buturovic25a.html}, 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 Instrument and embedded TriVerity 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.} }
Endnote
%0 Conference Paper %T Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis %A Ljubomir Buturovic %A Michael Mayhew %A Roland Luethy %A Kirindi Choi %A Uros Midic %A Nandita Damaraju %A Yehudit Hasin-Brumshtein %A Amitesh Pratap %A Rhys Adams %A Joao Fonseca %A Ambika Srinath %A Paul Fleming %A Claudia Pereira %A Oliver Liesenfeld %A Purvesh Khatri %A Timothy Sweeney %B Proceedings of the 4th Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2025 %E Stefan Hegselmann %E Helen Zhou %E Elizabeth Healey %E Trenton Chang %E Caleb Ellington %E Vishwali Mhasawade %E Sana Tonekaboni %E Peniel Argaw %E Haoran Zhang %F pmlr-v259-buturovic25a %I PMLR %P 154--170 %U https://proceedings.mlr.press/v259/buturovic25a.html %V 259 %X 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 Instrument and embedded TriVerity 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.
APA
Buturovic, L., Mayhew, M., Luethy, R., Choi, K., Midic, U., Damaraju, N., Hasin-Brumshtein, Y., Pratap, A., Adams, R., Fonseca, J., Srinath, A., Fleming, P., Pereira, C., Liesenfeld, O., Khatri, P. & Sweeney, T.. (2025). Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis. Proceedings of the 4th Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 259:154-170 Available from https://proceedings.mlr.press/v259/buturovic25a.html.

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