Construction of a Machine Learning-Based Risk Scoring Tool for Post-traumatic Acute Hypotension

Tingting Li (), Ruiqi Jia () and Jing Li ()
Additional contact information
Tingting Li: Beijing Jiaotong University
Ruiqi Jia: Beijing Jiaotong University
Jing Li: Beijing Jiaotong University

A chapter in LISS 2023, 2024, pp 244-255 from Springer

Abstract: Abstract Acute hypotension episodes (AHE) are the most common adverse events in post-traumatic emergencies, and the occurrence of acute hypotension is very dangerous for the life safety of critically ill patients. This can lead to fainting or shock, irreversible organ damage or even death. This study uses the mimic IV database to screen key indicators for acute post-traumatic hypotension using the XGBoost method, to select vital signs indicators based on timeliness, economy and convenience of clinical application to construct a simple hypotension scoring tool, and to specify the rationale and process for the construction of the scoring tool. The scoring of the condition can also assist healthcare professionals in the rapid diagnosis of the cause of the patient’s condition. The use of specific scores to indicate the extent of a patient’s condition not only facilitates patient understanding, but also allows the system to be used more frequently by doctors in the clinical setting. The results show that a risk of traumatic haemorrhagic shock scoring tool based on five vital signs - systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate and blood pressure - can accurately identify patient outcomes and differentiate the severity of injuries, providing some clinical decision support.

Keywords: Post-traumatic hypotension; risk scoring tool; key indicator screening; machine learning (search for similar items in EconPapers)
Date: 2024
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:978-981-97-4045-1_19

Ordering information: This item can be ordered from
http://www.springer.com/9789819740451

DOI: 10.1007/978-981-97-4045-1_19

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 ().