使用机器学习方法预测 1 型糖尿病成人的糖尿病视网膜病变:一项探索性研究

IF 4.1 Q2 ENDOCRINOLOGY & METABOLISM
Eslam Montaser, Viral N Shah
{"title":"使用机器学习方法预测 1 型糖尿病成人的糖尿病视网膜病变:一项探索性研究","authors":"Eslam Montaser, Viral N Shah","doi":"10.1177/19322968241292369","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T1D). This exploratory study uses machine learning on continuous glucose monitoring (CGM) data to identify factors influencing DR and predict high-risk individuals for timely intervention.</p><p><strong>Methods: </strong>Between June 2018 and March 2022, adults with T1D with incident DR or no retinopathy (control) were identified. The CGM data were collected retrospectively for up to seven years before the date of defining incident DR or no retinopathy. A mixture of three machine learning algorithms was trained and evaluated in two different scenarios, using different glycemic features extracted from CGM traces (scenario 1), and the two principal components (two PCs; exposure to hyperglycemia and hypoglycemia risk) of those features (scenario 2). Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model.</p><p><strong>Results: </strong>The CGM data of 30 adults with incident DR (mean±SD age of 21.2±9.4 years, glycated hemoglobin [HbA<sub>1c</sub>] of 8.6%±1.0%, and body mass index [BMI] of 24.5±4.8 kg/m<sup>2</sup>) and 30 adults without DR (age of 41.8±14.7 years, HbA<sub>1c</sub> of 7.0%±0.9%, and BMI of 26.2±3.6 kg/m<sup>2</sup>) were included in this analysis. In scenario 2, classifiers outperformed scenario 1, resulting in an average AUC-ROC increase to 0.92 for two of three models, indicating that the two PCs captured vital classification data, representing the most discriminative aspects and enhancing model performance.</p><p><strong>Conclusion: </strong>Machine learning approaches using CGM data may have potential to aid in identifying adults with T1D at risk of DR.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968241292369"},"PeriodicalIF":4.1000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571610/pdf/","citationCount":"0","resultStr":"{\"title\":\"Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.\",\"authors\":\"Eslam Montaser, Viral N Shah\",\"doi\":\"10.1177/19322968241292369\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T1D). This exploratory study uses machine learning on continuous glucose monitoring (CGM) data to identify factors influencing DR and predict high-risk individuals for timely intervention.</p><p><strong>Methods: </strong>Between June 2018 and March 2022, adults with T1D with incident DR or no retinopathy (control) were identified. The CGM data were collected retrospectively for up to seven years before the date of defining incident DR or no retinopathy. A mixture of three machine learning algorithms was trained and evaluated in two different scenarios, using different glycemic features extracted from CGM traces (scenario 1), and the two principal components (two PCs; exposure to hyperglycemia and hypoglycemia risk) of those features (scenario 2). Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model.</p><p><strong>Results: </strong>The CGM data of 30 adults with incident DR (mean±SD age of 21.2±9.4 years, glycated hemoglobin [HbA<sub>1c</sub>] of 8.6%±1.0%, and body mass index [BMI] of 24.5±4.8 kg/m<sup>2</sup>) and 30 adults without DR (age of 41.8±14.7 years, HbA<sub>1c</sub> of 7.0%±0.9%, and BMI of 26.2±3.6 kg/m<sup>2</sup>) were included in this analysis. In scenario 2, classifiers outperformed scenario 1, resulting in an average AUC-ROC increase to 0.92 for two of three models, indicating that the two PCs captured vital classification data, representing the most discriminative aspects and enhancing model performance.</p><p><strong>Conclusion: </strong>Machine learning approaches using CGM data may have potential to aid in identifying adults with T1D at risk of DR.</p>\",\"PeriodicalId\":15475,\"journal\":{\"name\":\"Journal of Diabetes Science and Technology\",\"volume\":\" \",\"pages\":\"19322968241292369\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571610/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Diabetes Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/19322968241292369\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Diabetes Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/19322968241292369","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0

摘要

背景:早期发现和干预对于预防威胁1型糖尿病(T1D)成人视力的糖尿病视网膜病变(DR)至关重要。这项探索性研究利用连续血糖监测(CGM)数据的机器学习来识别影响糖尿病视网膜病变的因素,并预测高风险个体,以便及时干预:在 2018 年 6 月至 2022 年 3 月期间,确定了患有 T1D 且发生 DR 或无视网膜病变(对照组)的成人。CGM数据是在确定发生DR或无视网膜病变的日期之前长达七年的回顾性收集。使用从 CGM 跟踪数据中提取的不同血糖特征(方案 1)和这些特征的两个主成分(两个 PC:高血糖暴露和低血糖风险)(方案 2),在两种不同的情况下对三种机器学习算法的混合物进行了训练和评估。分类器通过 10 倍交叉验证使用曲线下接收器操作特征面积(AUC-ROC)进行评估,以选出最佳分类模型:结果:本次分析包括了 30 名患有偶发性 DR 的成人(平均年龄(±SD)为 21.2±9.4 岁,糖化血红蛋白 [HbA1c] 为 8.6%±1.0%,体重指数 [BMI] 为 24.5±4.8 kg/m2)和 30 名未患有 DR 的成人(年龄为 41.8±14.7 岁,HbA1c 为 7.0%±0.9%,体重指数为 26.2±3.6 kg/m2)的 CGM 数据。在方案 2 中,分类器的表现优于方案 1,三个模型中有两个模型的平均 AUC-ROC 提高到了 0.92,这表明两个 PC 捕捉到了重要的分类数据,代表了最具区分度的方面,提高了模型的性能:结论:使用 CGM 数据的机器学习方法可能有助于识别有 DR 风险的成人 T1D 患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.

Background: Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T1D). This exploratory study uses machine learning on continuous glucose monitoring (CGM) data to identify factors influencing DR and predict high-risk individuals for timely intervention.

Methods: Between June 2018 and March 2022, adults with T1D with incident DR or no retinopathy (control) were identified. The CGM data were collected retrospectively for up to seven years before the date of defining incident DR or no retinopathy. A mixture of three machine learning algorithms was trained and evaluated in two different scenarios, using different glycemic features extracted from CGM traces (scenario 1), and the two principal components (two PCs; exposure to hyperglycemia and hypoglycemia risk) of those features (scenario 2). Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model.

Results: The CGM data of 30 adults with incident DR (mean±SD age of 21.2±9.4 years, glycated hemoglobin [HbA1c] of 8.6%±1.0%, and body mass index [BMI] of 24.5±4.8 kg/m2) and 30 adults without DR (age of 41.8±14.7 years, HbA1c of 7.0%±0.9%, and BMI of 26.2±3.6 kg/m2) were included in this analysis. In scenario 2, classifiers outperformed scenario 1, resulting in an average AUC-ROC increase to 0.92 for two of three models, indicating that the two PCs captured vital classification data, representing the most discriminative aspects and enhancing model performance.

Conclusion: Machine learning approaches using CGM data may have potential to aid in identifying adults with T1D at risk of DR.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Diabetes Science and Technology
Journal of Diabetes Science and Technology Medicine-Internal Medicine
CiteScore
7.50
自引率
12.00%
发文量
148
期刊介绍: The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信