Fuzzy Logic in neurosurgery: predicting poor outcomes after lumbar disk surgery in 501 consecutive patients

Muhammad Shahzad Shamim MBBS, MCPS, MRCS (Glasgow), FCPS (Neurosurgery) , Syed Ather Enam MBBS, MD, PhD, FRCS (Ire), FRCS (SN, CAN), DABS, FACS , Uvais Qidwai BE, ME, PhD
{"title":"Fuzzy Logic in neurosurgery: predicting poor outcomes after lumbar disk surgery in 501 consecutive patients","authors":"Muhammad Shahzad Shamim MBBS, MCPS, MRCS (Glasgow), FCPS (Neurosurgery) ,&nbsp;Syed Ather Enam MBBS, MD, PhD, FRCS (Ire), FRCS (SN, CAN), DABS, FACS ,&nbsp;Uvais Qidwai BE, ME, PhD","doi":"10.1016/j.surneu.2009.07.012","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Despite a lot of research into patient selection, a significant number of patients fail to benefit from surgery for symptomatic lumbar disk herniation. We have used Fuzzy Logic-based fuzzy inference system (FIS) for identifying patients unlikely to improve after disk surgery and explored FIS as a tool for surgical outcome prediction.</p></div><div><h3>Methods</h3><p>Data of 501 patients were retrospectively reviewed for 54 independent variables. Sixteen variables were short-listed based on heuristics and were further classified into memberships with degrees of membership within each. A set of 11 rules was formed, and the rule base used individual membership degrees and their values mapped from the membership functions to perform Boolean Logical inference for a particular set of inputs. For each rule, a decision bar was generated that, when combined with the other rules in a similar way, constituted a decision surface. The FIS decisions were then based on calculating the centroid for the resulting decision surfaces and thresholding of actual centroid values. The results of FIS were then compared with eventual postoperative patient outcomes based on clinical follow-ups at 6 months to evaluate FIS as a predictor of poor outcome.</p></div><div><h3>Results</h3><p>Fuzzy inference system has a sensitivity of 88% and specificity of 86% in the prediction of patients most likely to have poor outcome after lumbosacral miscrodiskectomy. The test thus has a positive predictive value of 0.36 and a negative predictive value of 0.98.</p></div><div><h3>Conclusion</h3><p>Fuzzy inference system is a sensitive method of predicting patients who will fail to improve with surgical intervention.</p></div>","PeriodicalId":22153,"journal":{"name":"Surgical Neurology","volume":"72 6","pages":"Pages 565-572"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.surneu.2009.07.012","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Surgical Neurology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0090301909006211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28

Abstract

Background

Despite a lot of research into patient selection, a significant number of patients fail to benefit from surgery for symptomatic lumbar disk herniation. We have used Fuzzy Logic-based fuzzy inference system (FIS) for identifying patients unlikely to improve after disk surgery and explored FIS as a tool for surgical outcome prediction.

Methods

Data of 501 patients were retrospectively reviewed for 54 independent variables. Sixteen variables were short-listed based on heuristics and were further classified into memberships with degrees of membership within each. A set of 11 rules was formed, and the rule base used individual membership degrees and their values mapped from the membership functions to perform Boolean Logical inference for a particular set of inputs. For each rule, a decision bar was generated that, when combined with the other rules in a similar way, constituted a decision surface. The FIS decisions were then based on calculating the centroid for the resulting decision surfaces and thresholding of actual centroid values. The results of FIS were then compared with eventual postoperative patient outcomes based on clinical follow-ups at 6 months to evaluate FIS as a predictor of poor outcome.

Results

Fuzzy inference system has a sensitivity of 88% and specificity of 86% in the prediction of patients most likely to have poor outcome after lumbosacral miscrodiskectomy. The test thus has a positive predictive value of 0.36 and a negative predictive value of 0.98.

Conclusion

Fuzzy inference system is a sensitive method of predicting patients who will fail to improve with surgical intervention.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
神经外科中的模糊逻辑:预测501例连续患者腰椎间盘手术后不良预后
背景:尽管对患者选择进行了大量研究,但相当多的患者未能从症状性腰椎间盘突出症的手术中获益。我们使用基于模糊逻辑的模糊推理系统(FIS)来识别椎间盘手术后不太可能改善的患者,并探索FIS作为手术结果预测的工具。方法回顾性分析501例患者的54个自变量。根据启发式方法列出了16个变量,并根据每个变量的隶属度进一步分类为隶属度。形成了一组11条规则,规则库使用单个隶属度及其从隶属度函数映射的值对一组特定输入执行布尔逻辑推理。对于每个规则,生成一个决策条,当它以类似的方式与其他规则组合时,就构成了一个决策面。然后,FIS决策基于计算结果决策曲面的质心和实际质心值的阈值。然后将FIS结果与基于6个月临床随访的最终术后患者结果进行比较,以评估FIS作为不良预后的预测因子。结果模糊推理系统对腰椎间盘切除术后预后不良患者的预测敏感性为88%,特异性为86%。因此,该测试的阳性预测值为0.36,阴性预测值为0.98。结论模糊推理系统是一种较为灵敏的预测手术治疗后病情无法好转的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Surgical Neurology
Surgical Neurology 医学-临床神经学
自引率
0.00%
发文量
0
期刊最新文献
Cardiac ventricular myosin and slow skeletal myosin exhibit dissimilar chemomechanical properties despite bearing the same myosin heavy chain isoform. Moyamoya disease. PNIPAAM modified mesoporous hydroxyapatite for sustained osteogenic drug release and promoting cell attachment. Biomedical research Editorial Board
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1