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.
Diagnostics 2019, 9(1), 20; https://doi.org/10.3390/diagnostics9010020
Received: 25 January 2019 / Revised: 6 February 2019 / Accepted: 11 February 2019 / Published: 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-TextKeywords:
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).
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.
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
Comments
[Return to top]
Diagnostics
EISSN 2075-4418
Published by MDPI AG, Basel, Switzerland
RSS
E-Mail Table of Contents Alert