Research on Time Window Prediction and Scoring Model for Trauma-Related Sepsis

Ke Luo (), Jing Li () and Yuzhuo Zhao ()
Additional contact information
Ke Luo: Beijing Jiaotong University
Jing Li: Beijing Jiaotong University
Yuzhuo Zhao: Chinese PLA General Hospital

A chapter in LISS 2021, 2022, pp 111-123 from Springer

Abstract: Abstract Based on the MIMIC-III database of the Massachusetts Institute of Technology, this paper studies and analyzes the symptoms of trauma-related sepsis. Use SOFA score as the Inclusion and Exclusion Criteria, extract the relevant patient medical index data with the guidance of a professional clinician. Sequential forward search is applied to search the optimal index combination based on the eXtreme Gradient Boosting (XGBoost) algorithm. Twenty independent replicates perform to obtain 7 key risk indicators (Urea Nitrogen, Prothrombin Time, PO2, Sodium, Red Blood Cells, Carbon Dioxide, International Normalized Ratio). The time window prediction model builds by four machine learning algorithms (decision tree, random forest, decision tree-based adaptive reinforcement (Adaboost) algorithm, XGBoost). The results show that the time window prediction model of trauma-related sepsis has good generalization ability. The prediction effect of the random forest and XGBoost algorithm is better than the other two. Finally, using the multi-factor Logistic regression method build the risk scoring tool for sepsis-induced by trauma-related infection base on the key risk indicators and the opinions of professional clinicians. The results show that the data-driven risk scoring tool can effectively predict the outcome of patients with trauma-related sepsis, which has high clinical significance.

Keywords: Trauma-related Sepsis; Big data; Key risk indicators; Time window forecast; Risk score; Machine learning (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-16-8656-6_10

Ordering information: This item can be ordered from
http://www.springer.com/9789811686566

DOI: 10.1007/978-981-16-8656-6_10

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().