Application of machine learning algorithm for the prediction of lupus nephritis using SNP data, polygenic risk score, and electronic health record.

IF 2.3 3区医学 Q2 HEALTH CARE SCIENCES & SERVICES

Health Informatics Journal Pub Date : 2025-07-01 Epub Date: 2025-08-06 DOI:10.1177/14604582251363510

Chih-Wei Chung, Seng-Cho Chou, Chung-Mao Kao, Yen-Ju Chen, Tzu-Hung Hsiao, Yi-Ming Chen

{"title":"Application of machine learning algorithm for the prediction of lupus nephritis using SNP data, polygenic risk score, and electronic health record.","authors":"Chih-Wei Chung, Seng-Cho Chou, Chung-Mao Kao, Yen-Ju Chen, Tzu-Hung Hsiao, Yi-Ming Chen","doi":"10.1177/14604582251363510","DOIUrl":null,"url":null,"abstract":"Background: Lupus nephritis (LN) flares raise the risks of renal failure and mortality in systemic lupus erythematosus (SLE) patients, making risk stratification and individualized care crucial. Our goal was to develop machine learning (ML) models to predict LN flares.Methods: A total of 1546 SLE patients were enrolled from a hospital-based cohort. Electronic health record (EHR), single nucleotide polymorphism (SNP), and polygenic risk score (PRS) were combined to construct ML models. SHapley Additive exPlanation (SHAP) values were calculated to assess each feature's contribution.Results: Within 5 years, 448 patients developed LN. Of the 686,354 SNPs, 375 were used for PRS computation. The model combining EHR, SNP, and PRS achieved the highest AUROC of 0.9512 and AUPRC of 0.8902 in validation, while the XGB-based hybrid model reached an AUPRC of 0.9021 in testing. The SHAP summary plot highlighted the top 20 features predicting LN flares.Conclusions: This hybrid model combining SNP, PRS, and EHR predicts active LN and requires validation.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"31 3","pages":"14604582251363510"},"PeriodicalIF":2.3000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Informatics Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/14604582251363510","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/6 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Lupus nephritis (LN) flares raise the risks of renal failure and mortality in systemic lupus erythematosus (SLE) patients, making risk stratification and individualized care crucial. Our goal was to develop machine learning (ML) models to predict LN flares.

Methods: A total of 1546 SLE patients were enrolled from a hospital-based cohort. Electronic health record (EHR), single nucleotide polymorphism (SNP), and polygenic risk score (PRS) were combined to construct ML models. SHapley Additive exPlanation (SHAP) values were calculated to assess each feature's contribution.

Results: Within 5 years, 448 patients developed LN. Of the 686,354 SNPs, 375 were used for PRS computation. The model combining EHR, SNP, and PRS achieved the highest AUROC of 0.9512 and AUPRC of 0.8902 in validation, while the XGB-based hybrid model reached an AUPRC of 0.9021 in testing. The SHAP summary plot highlighted the top 20 features predicting LN flares.

Conclusions: This hybrid model combining SNP, PRS, and EHR predicts active LN and requires validation.

查看原文本刊更多论文

利用SNP数据、多基因风险评分和电子健康记录，应用机器学习算法预测狼疮性肾炎。

背景：狼疮肾炎（LN）耀斑增加了系统性红斑狼疮（SLE）患者肾功能衰竭和死亡率的风险，因此风险分层和个体化护理至关重要。我们的目标是开发机器学习（ML）模型来预测LN耀斑。方法：从以医院为基础的队列中共入组1546例SLE患者。结合电子健康记录（EHR）、单核苷酸多态性（SNP）和多基因风险评分（PRS）构建ML模型。计算SHapley加性解释（SHAP）值来评估每个特征的贡献。结果：5年内，448例患者发生LN。在686,354个snp中，375个用于PRS计算。结合EHR、SNP和PRS的模型在验证中AUROC最高，为0.9512，AUPRC为0.8902，而基于xgb的混合模型在测试中AUPRC为0.9021。SHAP总结图突出了预测LN耀斑的前20个特征。结论：这种结合SNP、PRS和EHR的混合模型预测了活跃LN，需要验证。

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分钟内获得全文求助全文

来源期刊

Health Informatics Journal HEALTH CARE SCIENCES & SERVICES-MEDICAL INFORMATICS

CiteScore

7.80

自引率

6.70%

发文量

审稿时长

6 months

期刊介绍： Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.