Abstract:
Introduction:
Despite high associated mortality and high treatment costs,
sepsis remains difficult to diagnose. A recent supplement to
sepsis management are systems based on machine learning
(ML). Purpose:
Proof of concept and presentation of a MLbased clinical application for the early
prediction of sepsis. Material and methods:
The data comes from the publicly accessible database Early
Prediction of Sepsis from Clinical Data - the PhysioNet
Computing in Cardiology Challenge 2019 and include 40366
intensive care clinical cases, of which 7.26% are patients with
sepsis, and 92.74% - with other diagnoses. Exploratory data
analysis and data processing are performed in RStudio (R
programming language), and machine learning is based on the
H2O platform (www.h2o.ai). Results:
Based on the processing of
the large data set, an
intelligent system is built,
which allows the prediction
of sepsis 4 hours before the
onset and which can be
delivered as an application
for clinical use. The
performance metrics are:
accuracy - 0.91, specificity -
0.93 and sensitivity - 0.84.
Description:
Valeriu Ghereg Department of Anesthesiology an Intensive Care no. 1, Nicolae Testemitanu SUMPh,
Institute of Emergency Medicine
