Machine learning applications for the prediction of surgical site infection in neurological operations

接收机工作特性 医学 决策树 机器学习 朴素贝叶斯分类器 人工智能 贝叶斯定理 人工神经网络 回顾性队列研究 外科 算法 计算机科学 支持向量机 内科学 贝叶斯概率
作者
Thara Tunthanathip,Sakchai Sae-heng,Thakul Oearsakul,Ittichai Sakarunchai,Anukoon Kaewborisutsakul,Chin Taweesomboonyat
出处
期刊:Neurosurgical Focus [American Association of Neurological Surgeons]
卷期号:47 (2): E7-E7 被引量:50
标识
DOI:10.3171/2019.5.focus19241
摘要

Surgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspects. The implementation of ML algorithms to learn from medical data may help in obtaining prognostic information on diseases, especially SSIs. The purpose of this study was to compare the performance of various ML models for predicting surgical infection after neurosurgical operations.A retrospective cohort study was conducted on patients who had undergone neurosurgical operations at tertiary care hospitals between 2010 and 2017. Supervised ML algorithms, which included decision tree, naive Bayes with Laplace correction, k-nearest neighbors, and artificial neural networks, were trained and tested as binary classifiers (infection or no infection). To evaluate the ML models from the testing data set, their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as their accuracy, receiver operating characteristic curve, and area under the receiver operating characteristic curve (AUC) were analyzed.Data were available for 1471 patients in the study period. The SSI rate was 4.6%, and the type of SSI was superficial, deep, and organ/space in 1.2%, 0.8%, and 2.6% of cases, respectively. Using the backward stepwise method, the authors determined that the significant predictors of SSI in the multivariable Cox regression analysis were postoperative CSF leakage/subgaleal collection (HR 4.24, p < 0.001) and postoperative fever (HR 1.67, p = 0.04). Compared with other ML algorithms, the naive Bayes had the highest performance with sensitivity at 63%, specificity at 87%, PPV at 29%, NPV at 96%, and AUC at 76%.The naive Bayes algorithm is highlighted as an accurate ML method for predicting SSI after neurosurgical operations because of its reasonable accuracy. Thus, it can be used to effectively predict SSI in individual neurosurgical patients. Therefore, close monitoring and allocation of treatment strategies can be informed by ML predictions in general practice.
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
PDF的下载单位、IP信息已删除 (2025-6-4)

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
赖道之发布了新的文献求助10
刚刚
一区的王完成签到 ,获得积分10
1秒前
搬砖的化学男完成签到 ,获得积分0
2秒前
满意的迎南完成签到 ,获得积分10
2秒前
wang完成签到,获得积分10
2秒前
Lynn完成签到 ,获得积分10
8秒前
踏实的无敌完成签到,获得积分10
10秒前
11秒前
星辰大海应助淡然的熊猫采纳,获得10
18秒前
piaoaxi完成签到 ,获得积分10
19秒前
mol完成签到 ,获得积分10
19秒前
20秒前
烊烊完成签到,获得积分10
23秒前
yang杨完成签到,获得积分10
23秒前
thuuu完成签到,获得积分10
25秒前
INBI发布了新的文献求助30
25秒前
Catherkk完成签到,获得积分10
25秒前
粱乘风完成签到,获得积分10
25秒前
Belinda完成签到 ,获得积分10
26秒前
AN完成签到,获得积分10
27秒前
myg123完成签到 ,获得积分10
29秒前
Seth完成签到,获得积分10
30秒前
30秒前
神勇友灵完成签到,获得积分10
30秒前
WL完成签到 ,获得积分10
34秒前
36秒前
qcl完成签到,获得积分10
40秒前
42秒前
淡然的熊猫完成签到,获得积分10
42秒前
顺心的安珊完成签到 ,获得积分10
43秒前
44秒前
vsvsgo发布了新的文献求助10
44秒前
负责的白风完成签到,获得积分10
45秒前
shy完成签到,获得积分10
46秒前
life完成签到,获得积分10
47秒前
夏姬宁静完成签到,获得积分10
48秒前
高兴的凝蝶完成签到,获得积分10
49秒前
阿鑫完成签到 ,获得积分10
49秒前
CipherSage应助毅诚菌采纳,获得10
49秒前
xuzj应助科研通管家采纳,获得10
49秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 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 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038235
求助须知:如何正确求助?哪些是违规求助? 3575992
关于积分的说明 11374009
捐赠科研通 3305760
什么是DOI,文献DOI怎么找? 1819276
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022