Using Machine Learning Methods to Predict the Lactate Trend of Sepsis Patients in the ICU

Mustafa Kemal Arslantas, Tunc Asuroglu (Corresponding author), Reyhan Arslantas, Emin Pashazade, Pelin Corman Dincer, Gulbin Tore Altun, Alper Kararmaz

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientific

Abstract

Serum lactate levels are considered a biomarker of tissue hypoxia. In sepsis or septic shock patients, as suggested by The Surviving Sepsis Campaign, early lactate clearance-directed therapy is associated with decreased mortality; thus, serum lactate levels should be assessed. Monitoring a patient’s vital parameters and repetitive blood analysis may have deleterious effects on the patient and also bring an economic burden. Machine learning and trend analysis are gaining importance to overcome these issues. In this context, we aimed to investigate if a machine learning approach can predict lactate trends from non-invasive parameters of patients with sepsis. This retrospective study analyzed adult sepsis patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset. Inclusion criteria were two or more lactate tests within 6 h of diagnosis, an ICU stay of at least 24 h, and a change of ≥1 mmol/liter in lactate level. Naïve Bayes, J48 Decision Tree, Logistic Regression, Random Forest, and Logistic Model Tree (LMT) classifiers were evaluated for lactate trend prediction. LMT algorithm outperformed other classifiers (AUC = 0.803; AUPRC = 0.921). J48 decision tree performed worse than the other methods when predicting constant trend. LMT algorithm with four features (heart rate, oxygen saturation, initial lactate, and time interval variables) achieved 0.80 in terms of AUC (AUPRC = 0.921). We can say that machine learning models that employ logistic regression architectures, i.e., LMT algorithm achieved good results in lactate trend prediction tasks, and it can be effectively used to assess the state of the patient, whether it is stable or improving.
Original languageEnglish
Title of host publicationDigital Health and Wireless Solutions
Subtitle of host publicationFirst Nordic Conference​, NCDHWS 2024, Oulu, Finland, May 7–8, 2024, Proceedings, Part I
PublisherSpringer
Pages3-16
Number of pages14
ISBN (Electronic)978-3-031-59079-5
ISBN (Print)978-3-031-59090-0
DOIs
Publication statusPublished - 5 May 2024
MoE publication typeB2 Part of a book or another research book
Event1st Nordic Conference on Digital Health and Wireless Solutions, NCDHWS 2024 - Hotel Lasaretti, Oulu, Finland
Duration: 7 May 20248 May 2024
https://nordic-digihealth.com/welcome/

Publication series

SeriesCommunications in Computer and Information Science
Volume2084
ISSN1865-0929

Conference

Conference1st Nordic Conference on Digital Health and Wireless Solutions, NCDHWS 2024
Country/TerritoryFinland
CityOulu
Period7/05/248/05/24
Internet address

Keywords

  • Intensive Care Unit
  • Machine Learning
  • Sepsis
  • Serum Lactate Value

Fingerprint

Dive into the research topics of 'Using Machine Learning Methods to Predict the Lactate Trend of Sepsis Patients in the ICU'. Together they form a unique fingerprint.

Cite this