您的位置: 首页 > 2023年7月 第54卷 第7期 > 文字全文
2023年7月 第38卷 第7期11
目录

基于随机森林算法建立甲状腺功能减退患病风险预测模型

Establishing a hypothyroidism risk prediction model based on random forest algorithm

来源期刊: 广州医药 | 16-24 发布时间:2023-08-15 收稿时间:2025/11/13 18:32:23 阅读量:24
作者:
关键词:
甲状腺功能减退症随机森林预测模型MIMIC-IV数据库
hypothyroidismrandom forestpredictive modelMIMIC-IV database
DOI:
10.3969/j.issn.1000-8535.2023.07.003
收稿时间:
2023-01-13 
修订日期:
 
接收日期:
 
引用总数:
3  
目的 基于随机森林方法构建甲状腺功能减退(简称甲减)患病风险预测模型。方法 从MIMIC-IV数据库纳入5 735名甲减患者为病例组,4 803名非甲减患者为对照组,基于随机森林模型进行建模。同时利用逻辑回归、贝叶斯正则化神经网络、XGBoost作为比较模型。最后用准确率、F1分数、精确率、召回率、特异性以及AUC值评价四个机器学习模型性能。结果 随机森林模型准确率为0.85,F1分数为0.84,精确率为0.84,召回率为0.84,特异性为0.86,AUC值为0.91。在该模型中,促甲状腺激素、年龄、绝对淋巴细胞计数、血液中红细胞数、中性白细胞、性别、碱性磷酸酶、丙氨酸氨基转移酶、嗜酸性粒细胞绝对计数、尿素氮为甲减患者诊断重要性排前10的指标。结论 采用随机森林方法构建的甲减患病预测模型为甲减的早期诊断有潜在应用价值。
Objective To construct a risk prediction model for hypothyroidism based on the random forest model.Methods A total of 5 735 hypothyroidism patients were included from the MIMIC-IV database as the case group, and 4 803 non-hypothyroidism patients were included as the control group.Random forest models were constructed for both groups, and logistic regression, Bayesian regularized neural network, and XGBoost were used as comparative models.The performance of the four machine learning models was evaluated using accuracy, F1 score, precision, recall, specificity, and AUC value.Results The random forest model had an accuracy of 0.85, an F1 score of 0.84, a precision of 0.84, a recall of 0.84, a specificity of 0.86, and an AUC value of 0.91.In this model, thyroid-stimulating hormone, age, absolute lymphocyte count, red blood cell count in blood, neutrophil, gender, alkaline phosphatase, aspartate aminotransferase, absolute eosinophil count, and blood urea nitrogen were the top 10 indicators for diagnosing hypothyroidism patients.Conclusions The hypothyroidism disease prediction model constructed using the random forest method has potential application value for the early diagnosis of hypothyroidism.
1、 CHAKER L,RAZVI S,BENSENOR I M,et al.Hypothyroidism[J].Nat Rev Dis Primers,2022,8(1):30. CHAKER L,RAZVI S,BENSENOR I M,et al.Hypothyroidism[J].Nat Rev Dis Primers,2022,8(1):30.
2、 JABBAR A,PINGITORE A,PEARCE S H S,et al.Thyroid hormones and cardiovascular disease[J].Nature Reviews Cardiology,2017,14(1):39-55. JABBAR A,PINGITORE A,PEARCE S H S,et al.Thyroid hormones and cardiovascular disease[J].Nature Reviews Cardiology,2017,14(1):39-55.
3、 SISKIND S M,LEE S Y,PEARCE E N.Investigating hypothyroidism[J].BMJ,2021,1(373):n993. SISKIND S M,LEE S Y,PEARCE E N.Investigating hypothyroidism[J].BMJ,2021,1(373):n993.
4、 KIM W,CHO Y A,KIM D C,et al.Factors associated with thyroid-related adverse events in patients receiving PD-1 or PD-L1 inhibitors using machine learning models[J].Cancers,2021,13(21):5465. KIM W,CHO Y A,KIM D C,et al.Factors associated with thyroid-related adverse events in patients receiving PD-1 or PD-L1 inhibitors using machine learning models[J].Cancers,2021,13(21):5465.
5、 CHEN T,GUESTRIN C.Xgboost:A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.2016:785-794. CHEN T,GUESTRIN C.Xgboost:A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.2016:785-794.
6、 BURDEN F,WINKLER D.Bayesian regularization of neural networks[J].Artificial neural networks:methods and applications,2009:23-42. BURDEN F,WINKLER D.Bayesian regularization of neural networks[J].Artificial neural networks:methods and applications,2009:23-42.
7、 HOSMER Jr D W,LEMESHOW S,STURDIVANT R X.Applied logistic regression[M].John Wiley & Sons,2013. HOSMER Jr D W,LEMESHOW S,STURDIVANT R X.Applied logistic regression[M].John Wiley & Sons,2013.
8、 STEKHOVEN D J,BüHLMANN P.MissForest—non-parametric missing value imputation for mixed-type data[J].Bioinformatics,2012,28(1):112-118. STEKHOVEN D J,BüHLMANN P.MissForest—non-parametric missing value imputation for mixed-type data[J].Bioinformatics,2012,28(1):112-118.
9、 GOLDBERGER A L,AMARAL L A N,GLASS L,et al.PhysioBank,PhysioToolkit,and PhysioNet:components of a new research resource for complex physiologic signals[J].Circulation,2000,101(23):e215-e220. GOLDBERGER A L,AMARAL L A N,GLASS L,et al.PhysioBank,PhysioToolkit,and PhysioNet:components of a new research resource for complex physiologic signals[J].Circulation,2000,101(23):e215-e220.
10、 JOHNSON A,BULGARELLI L,POLLARD T,et al.Data from:MIMIC-IV (version 2.0).2022[DB].http://physionet.org/content/mimiciv/2.0/ JOHNSON A,BULGARELLI L,POLLARD T,et al.Data from:MIMIC-IV (version 2.0).2022[DB].http://physionet.org/content/mimiciv/2.0/
11、 ZHANG H,ZHANG Z,LIU X,et al.DNA methylation haplotype block markers efficiently discriminate follicular thyroid carcinoma from follicular adenoma[J].J Clin Endocrinol Metab,2021,106(4):1011-1021. ZHANG H,ZHANG Z,LIU X,et al.DNA methylation haplotype block markers efficiently discriminate follicular thyroid carcinoma from follicular adenoma[J].J Clin Endocrinol Metab,2021,106(4):1011-1021.
12、 LUONG G,IDARRAGA AJ,HSIAO V,et al.Risk stratifying indeterminate thyroid nodules with machine learning[J].J Surg Res,2022,1(270):214-220. LUONG G,IDARRAGA AJ,HSIAO V,et al.Risk stratifying indeterminate thyroid nodules with machine learning[J].J Surg Res,2022,1(270):214-220.
13、 SAI P V,RAJALAKSHMI T,SNEKHALATHA U.Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers[J].Proc Inst Mech Eng H,2021,235(10):1128-1145. SAI P V,RAJALAKSHMI T,SNEKHALATHA U.Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers[J].Proc Inst Mech Eng H,2021,235(10):1128-1145.
14、 BREIMAN L.Random forests[J].Machine Learning,2001,45(1):5-32. BREIMAN L.Random forests[J].Machine Learning,2001,45(1):5-32.
15、 SHARMA A K,ARYA R,MEHTA R,et al.Hypothyroidism and cardiovascular disease:Factors,mechanism and future perspectives[J].Curr Med Chem,2013,20(35):4411-4418. SHARMA A K,ARYA R,MEHTA R,et al.Hypothyroidism and cardiovascular disease:Factors,mechanism and future perspectives[J].Curr Med Chem,2013,20(35):4411-4418.
16、 BOUCAI L,HOLLOWELL J G,SURKS M I.An approach for development of age-,gender-,and ethnicity-specific thyrotropin reference limits[J].Thyroid,2011,21(1):5-11. BOUCAI L,HOLLOWELL J G,SURKS M I.An approach for development of age-,gender-,and ethnicity-specific thyrotropin reference limits[J].Thyroid,2011,21(1):5-11.
17、 CHAKER L,BIANCO A C,JONKLAAS J,et al.Hypothyroidism[J].Lancet,2017,390(10101):1550-1562. CHAKER L,BIANCO A C,JONKLAAS J,et al.Hypothyroidism[J].Lancet,2017,390(10101):1550-1562.
18、 DUNTAS L H,YEN P M.Diagnosis and treatment of hypothyroidism in the elderly[J].Endocrine,2019,66(1):63-69. DUNTAS L H,YEN P M.Diagnosis and treatment of hypothyroidism in the elderly[J].Endocrine,2019,66(1):63-69.
1、李蝶.手术室感染监测与预警综合管理系统的设计与实现[D].中南大学,2024.DOI:10.27661/d.cnki.gzhnu.2024.003448. 李蝶.手术室感染监测与预警综合管理系统的设计与实现[D].中南大学,2024.DOI:10.27661/d.cnki.gzhnu.2024.003448.
2、江竹月,孙洲华,张庆庆,等.机器学习算法在术后谵妄风险评估中的应用进展[J].广州医药,2025,56(01):42-47.DOI:10.20223/j.cnki.1000-8535.2025.01.006. 江竹月,孙洲华,张庆庆,等.机器学习算法在术后谵妄风险评估中的应用进展[J].广州医药,2025,56(01):42-47.DOI:10.20223/j.cnki.1000-8535.2025.01.006.
3、张茅平,陈国东.构建基于MIMIC-Ⅳ数据库的主动脉夹层B型患者急性期死亡风险列线图预测模型:一项回顾性分析[J].广州医药,2025,56(08):1134-1144.DOI:10.20223/j.cnki.1000-8535.2025.08.018. 张茅平,陈国东.构建基于MIMIC-Ⅳ数据库的主动脉夹层B型患者急性期死亡风险列线图预测模型:一项回顾性分析[J].广州医药,2025,56(08):1134-1144.DOI:10.20223/j.cnki.1000-8535.2025.08.018.
上一篇
下一篇
出版者信息








《广州医药》公众号
目录