CASIA OpenIR  > 多模态人工智能系统全国重点实验室
Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A
Ai, Di1; Cui, Chang2,3; Tang, Yongqiang2; Wang, Yan4; Zhang, Ningning5; Zhang, Chenyang2; Zhen, Yingzi1; Li, Gang6; Huang, Kun1; Liu, Guoqing1; Chen, Zhenping6; Zhang, Wensheng2; Wu, Runhui1
Source PublicationTHROMBOSIS RESEARCH
ISSN0049-3848
2023-12-01
Volume232Pages:43-53
Corresponding AuthorTang, Yongqiang(yongqiang.tang@ia.ac.cn) ; Chen, Zhenping(chenzhenping@outlook.com) ; Zhang, Wensheng(zhangwenshengia@hotmail.com) ; Wu, Runhui(runhuiwu@hotmail.com)
AbstractBackground: Physical activity is a crucial part of an active lifestyle for haemophiliac children. However, the fear of bleeds has been identified as barriers to participating physical activity for haemophiliac children even with prophylaxis. Lack of evidence and metrics driven by data is key problem. Objectives: We aim to develop machine learning models based on clinical data with multiple potential factors considered to predict risk of physical activity bleeding for haemophilia children with prophylaxis. Methods: From this cohort study, we collected information on 98 haemophiliac children with adequate prophylaxis (trough FVIII:C level > 1 %). The involved potential predictor variables include demographic information, treatment information, physical activity, joint evaluation, and pharmacokinetic parameters, etc. We applied CoxPH, Random Survival Forests (RSF) and DeepSurv to construct prediction models for the risk of bleeding during physical activities. All three survival analysis models were internally and externally validated. Results: A total of 98 patients were enrolled in this study. Their median age was 7.9 (5.5, 10.2) years. The CoxPH, RSF and DeepSurv models' discriminative and calibration abilities were all high, and the RSF model had the best performance (Internal validation: C-index, 0.7648 +/- 0.0139; Brier Score, 0.1098 +/- 0.0015; External validation: C-index, 0.7260 +/- 0.0154; Brier Score, 0.0930 +/- 0.0018). The prediction curves demonstrated that the developed RSF model can distinguish the risks well between bleeding and non-bleeding patients, as well as patients with different levels of physical activity. Meanwhile, the feature importance analysis confirmed that physical activity bleeding was deduced by comprehensive effects of various factors, and the importance of different factors on bleeding outcome is discrepant. Conclusions: This study revealed from the mechanism that it is necessary to incorporate multiple factors to accurately predict physical activity related bleeding risk. In clinical practice, the designed machine learning models can provide guidance for children with haemophilia A to positively participate in physical activity.
KeywordHaemophilia Children Machine learning Bleeding predictive modelling Physical activity
DOI10.1016/j.thromres.2023.10.012
WOS KeywordYOUNG-PATIENTS ; CHILDREN ; PARTICIPATION ; PROPHYLAXIS
Indexed BySCI
Language英语
Funding ProjectResearch on the application of clinical characteristics of the Beijing Municipal Sci-ence and Technology Commission[Z181100001718182] ; Research on the application of clinical characteristics of the Beijing Municipal Sci-ence and Technology Commission[20006429]
Funding OrganizationResearch on the application of clinical characteristics of the Beijing Municipal Sci-ence and Technology Commission
WOS Research AreaHematology ; Cardiovascular System & Cardiology
WOS SubjectHematology ; Peripheral Vascular Disease
WOS IDWOS:001156025700001
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55503
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorTang, Yongqiang; Chen, Zhenping; Zhang, Wensheng; Wu, Runhui
Affiliation1.Capital Med Univ, Natl Key Discipline Pediat,Natl Ctr Childrens Hlt, Beijing Childrens Hosp,Minist Educ,Key Lab Major, Hematol Ctr,Beijing Key Lab Pediat Hematol Oncol,, Beijing 100045, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Dept Rehabil, Beijing, Peoples R China
5.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Dept Radiol, Beijing, Peoples R China
6.Capital Med Univ, Beijing Childrens Hosp, Beijing Pediat Res Inst, Hematol Dis Lab, Beijing 100045, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ai, Di,Cui, Chang,Tang, Yongqiang,et al. Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A[J]. THROMBOSIS RESEARCH,2023,232:43-53.
APA Ai, Di.,Cui, Chang.,Tang, Yongqiang.,Wang, Yan.,Zhang, Ningning.,...&Wu, Runhui.(2023).Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A.THROMBOSIS RESEARCH,232,43-53.
MLA Ai, Di,et al."Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A".THROMBOSIS RESEARCH 232(2023):43-53.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ai, Di]'s Articles
[Cui, Chang]'s Articles
[Tang, Yongqiang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ai, Di]'s Articles
[Cui, Chang]'s Articles
[Tang, Yongqiang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ai, Di]'s Articles
[Cui, Chang]'s Articles
[Tang, Yongqiang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.