Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury

逻辑回归 生命体征 机器学习 医学 算法 急性肾损伤 人工智能 肌酐 急诊医学 统计 数学 内科学 计算机科学 外科
作者
Joshua Parreco,Hahn Soe-Lin,Jonathan Parks,Saskya Byerly,Matthew Chatoor,Jessica L. Buicko,Nicholas Namias,Rishi Rattan
出处
期刊:American Surgeon [SAGE]
卷期号:85 (7): 725-729 被引量:35
标识
DOI:10.1177/000313481908500731
摘要

Prior studies have used vital signs and laboratory measurements with conventional modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to use the trend in vital signs and laboratory measurements with machine learning algorithms for predicting AKI in ICU patients. The eICU Collaborative Research Database was queried for five consecutive days of laboratory measurements per patient. Patients with AKI were identified and trends in vital signs and laboratory values were determined by calculating the slope of the least-squares-fit linear equation using three days for each value. Different machine learning classifiers (gradient boosted trees [GBT], logistic regression, and deep learning) were trained to predict AKI using the laboratory values, vital signs, and slopes. There were 151,098 ICU stays identified and the rate of AKI was 5.6 per cent. The best performing algorithm was GBT with an AUC of 0.834 ± 0.006 and an F-measure of 42.96 per cent ± 1.26 per cent. Logistic regression performed with an AUC of 0.827 ± 0.004 and an F-measure of 28.29 per cent ± 1.01 per cent. Deep learning performed with an AUC of 0.817 ± 0.005 and an F-measure of 42.89 per cent ± 0.91 per cent. The most important variable for GBT was the slope of the minimum creatinine (30.32%). This study identifies the best performing machine learning algorithms for predicting AKI using trends in laboratory values in ICU patients. Early identification of these patients using readily available data indicates that incorporating machine learning predictive models into electronic medical record systems is an inevitable requisite for improving patient outcomes.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI2S应助豆包采纳,获得10
刚刚
共享精神应助豆包采纳,获得10
刚刚
AM发布了新的文献求助10
刚刚
刚刚
术言完成签到,获得积分10
2秒前
王十三发布了新的文献求助10
3秒前
3秒前
4秒前
公子襄由于求助违规,被管理员扣积分20
4秒前
4秒前
小小佳作完成签到,获得积分10
4秒前
和谐尔阳发布了新的文献求助10
5秒前
5秒前
6秒前
7秒前
ab发布了新的文献求助10
7秒前
7秒前
科研通AI2S应助fengyuke采纳,获得10
7秒前
沉默凌波发布了新的文献求助10
8秒前
清秋完成签到,获得积分10
8秒前
wwxd发布了新的文献求助10
9秒前
alixy发布了新的文献求助10
9秒前
11秒前
超级月饼发布了新的文献求助10
11秒前
优美的风完成签到,获得积分10
11秒前
11秒前
星期五发布了新的文献求助30
13秒前
情怀应助飘逸的飞绿采纳,获得10
13秒前
13秒前
Akim应助一名混子王采纳,获得10
14秒前
aaaaaa发布了新的文献求助10
15秒前
15秒前
小小佳作发布了新的文献求助10
15秒前
15秒前
16秒前
共享精神应助YNHN采纳,获得30
19秒前
19秒前
19秒前
碎碎发布了新的文献求助20
19秒前
wwxd完成签到,获得积分10
20秒前
高分求助中
Rock-Forming Minerals, Volume 3C, Sheet Silicates: Clay Minerals 2000
The late Devonian Standard Conodont Zonation 2000
Nickel superalloy market size, share, growth, trends, and forecast 2023-2030 2000
The Lali Section: An Excellent Reference Section for Upper - Devonian in South China 1500
Very-high-order BVD Schemes Using β-variable THINC Method 910
Development of general formulas for bolted flanges, by E.O. Waters [and others] 600
A new species of Coccus (Homoptera: Coccoidea) from Malawi 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3264677
求助须知:如何正确求助?哪些是违规求助? 2904671
关于积分的说明 8331143
捐赠科研通 2574954
什么是DOI,文献DOI怎么找? 1399601
科研通“疑难数据库(出版商)”最低求助积分说明 654521
邀请新用户注册赠送积分活动 633205