Predicting Sepsis in the Intensive Care Unit (ICU) through Vital Signs using Support Vector Machine (SVM)

Q3 Computer Science

Open Bioinformatics Journal Pub Date : 2021-11-19 DOI:10.2174/18750362021140100108

Zeina Rayan, Marco Alfonse, Abdel-badeeh M. Salem

引用次数: 2

Abstract

As sepsis is one of the life-threatening diseases, predicting sepsis with high accuracy could help save lives. Efficiency and accuracy of predicting sepsis can be enhanced through optimal feature selection. In this work, a support vector machine model is proposed to automatically predict a patient’s risk of sepsis based on physiological data collected from the ICU. The support vector machine algorithm that uses the extracted features has a great impact on sepsis prediction, which yields the accuracy of 0.73. Predicting sepsis can be accurately performed using the main vital signs and support vector machine.

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基于生命体征的支持向量机(SVM)预测重症监护病房(ICU)脓毒症

由于败血症是一种危及生命的疾病，高精度预测败血症有助于挽救生命。通过优化特征选择可以提高预测败血症的效率和准确性。在这项工作中，基于从ICU收集的生理数据，提出了一种支持向量机模型来自动预测患者患败血症的风险。使用提取特征的支持向量机算法对败血症预测有很大影响，其准确率为0.73。使用主要生命体征和支持向量机可以准确预测败血症。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Open Bioinformatics Journal Computer Science-Computer Science (miscellaneous)

CiteScore

2.40

自引率

0.00%

发文量

期刊介绍： The Open Bioinformatics Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters, clinical trial studies and guest edited single topic issues in all areas of bioinformatics and computational biology. The coverage includes biomedicine, focusing on large data acquisition, analysis and curation, computational and statistical methods for the modeling and analysis of biological data, and descriptions of new algorithms and databases. The Open Bioinformatics Journal, a peer reviewed journal, is an important and reliable source of current information on the developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.