Next Article in Journal
Metabolomics Biomarkers of Prostate Cancer: A Systematic Review
Previous Article in Journal
Introduction to Special Issue on “Electromagnetic Technologies for Medical Diagnostics: Fundamental Issues, Clinical Applications and Perspectives”
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessArticle
Diagnostics 2019, 9(1), 20; https://doi.org/10.3390/diagnostics9010020

Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients

Dascena, Inc., Oakland, CA 94612, USA
*
Author to whom correspondence should be addressed.
Received: 25 January 2019 / Revised: 6 February 2019 / Accepted: 11 February 2019 / Published: 13 February 2019
Full-Text   |   PDF [593 KB, uploaded 13 February 2019]   |  

Abstract

Sepsis, a dysregulated host response to infection, is a major health burden in terms of both mortality and cost. The difficulties clinicians face in diagnosing sepsis, alongside the insufficiencies of diagnostic biomarkers, motivate the present study. This work develops a machine-learning-based sepsis diagnostic for a high-risk patient group, using a geographically and institutionally diverse collection of nearly 500,000 patient health records. Using only a minimal set of clinical variables, our diagnostics outperform common severity scoring systems and sepsis biomarkers and benefit from being available immediately upon ordering. View Full-Text
Keywords: sepsis; laboratory developed test; machine learning; clinical decision support; electronic health record; biomarker; medical informatics sepsis; laboratory developed test; machine learning; clinical decision support; electronic health record; biomarker; medical informatics
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Calvert, J.; Saber, N.; Hoffman, J.; Das, R. Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients. Diagnostics 2019, 9, 20.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Diagnostics EISSN 2075-4418 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top