个人信息
教师姓名：蔡璐璐
教师英文名称：Lulu Cai
教师拼音名称：Cai Lulu
电子邮箱：cailulu@med.uestc.edu.cn
学历：博士研究生毕业
性别：女
联系方式：cailulu@med.uestc.edu.cn
学位：工学博士学位
职称：教授
博士生导师
曾获荣誉：国家高层次青年人才，四川省青年五四奖章，四川省杰出青年基金，四川省医学科技奖一等奖，四川省学术技术带头人后备人，四川省高层次海外留学人才。中国抗癌协会纳米肿瘤学分会委员、中国医药生物技术协会纳米技术分会委员、四川省药学会纳米药物专委会副主任委员，Signal Transduct Target Ther和Asian J Pharm Sci等SCI杂志（青年）编委。
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所属院系： 医学院
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学科：生物医学工程
药剂学
其他联系方式
通讯/办公地址：
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论文成果
Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes
发布时间：2025-05-23 点击次数：
所属单位：[1]Univ Elect Sci & Technol China, Personalized Drug Therapy Key Lab Sichuan Prov, Sch Med, Chengdu, Peoples R China;[2]Sichuan Acad Med Sci & Sichuan Prov Peoples Hosp, Dept Pharm, Chengdu, Peoples R China;[3]Sichuan Univ, West China Med Coll, Chengdu, Peoples R China
发表刊物：FRONTIERS IN PHARMACOLOGY
关键字：type 2 diabetes; diabetic complications; HbA1c; patient nonadherence; machine learning
摘要：Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D). Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People＇s Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set. Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A >= 7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 +/- 0.040, 0.859 +/- 0.050, 0.889 +/- 0.059, 0.832 +/- 0.086, and 0.825 +/- 0.092, respectively. Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care.
文献类型：Article
卷号：12
ISSN号：1663-9812
是否译文：否
