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Cited by in F6Publishing
For: Chen C, Zhou J, Yu H, Zhang Q, Gao L, Yin X, Dong Y, Lin Y, Li D, Yang Y, Wang Y, Tse G, Xia Y. Identification of important risk factors for all-cause mortality of acquired long QT syndrome patients using random survival forests and non-negative matrix factorization. Heart Rhythm 2021;18:426-33. [PMID: 33127541 DOI: 10.1016/j.hrthm.2020.10.022] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Tang H, Jin Z, Deng J, She Y, Zhong Y, Sun W, Ren Y, Cao N, Chen C. Development and validation of a deep learning model to predict the survival of patients in ICU. J Am Med Inform Assoc 2022:ocac098. [PMID: 35751440 DOI: 10.1093/jamia/ocac098] [Reference Citation Analysis]
2 Lee S, Zhou J, Guo CL, Wong WT, Liu T, Wong ICK, Jeevaratnam K, Zhang Q, Tse G. Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death. Endocrinol Diabetes Metab 2021;4:e00240. [PMID: 34277965 DOI: 10.1002/edm2.240] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
3 Tse G, Li KHC, Cheung CKY, Letsas KP, Bhardwaj A, Sawant AC, Liu T, Yan GX, Zhang H, Jeevaratnam K, Sayed N, Cheng SH, Wong WT. Arrhythmogenic Mechanisms in Hypokalaemia: Insights From Pre-clinical Models. Front Cardiovasc Med 2021;8:620539. [PMID: 33614751 DOI: 10.3389/fcvm.2021.620539] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
4 Tse G, Lee S, Zhou J, Liu T, Wong ICK, Mak C, Mok NS, Jeevaratnam K, Zhang Q, Cheng SH, Wong WT. Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses. Front Cardiovasc Med 2021;8:608592. [PMID: 33614747 DOI: 10.3389/fcvm.2021.608592] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
5 Lee S, Zhou J, Li KHC, Leung KSK, Lakhani I, Liu T, Wong ICK, Mok NS, Mak C, Jeevaratnam K, Zhang Q, Tse G. Territory-wide cohort study of Brugada syndrome in Hong Kong: predictors of long-term outcomes using random survival forests and non-negative matrix factorisation. Open Heart 2021;8:e001505. [PMID: 33547222 DOI: 10.1136/openhrt-2020-001505] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
6 Gurung B, Tse G, Keung W, Li RA, Wong WT. Arrhythmic Risk Assessment of Hypokalaemia Using Human Pluripotent Stem Cell-Derived Cardiac Anisotropic Sheets. Front Cell Dev Biol 2021;9:681665. [PMID: 34938727 DOI: 10.3389/fcell.2021.681665] [Reference Citation Analysis]
7 Lee S, Zhou J, Jeevaratnam K, Wong WT, Wong ICK, Mak C, Mok NS, Liu T, Zhang Q, Tse G. Paediatric/young versus adult patients with long QT syndrome. Open Heart 2021;8:e001671. [PMID: 34518285 DOI: 10.1136/openhrt-2021-001671] [Reference Citation Analysis]
8 Lee S, Zhou J, Wong WT, Liu T, Wu WKK, Wong ICK, Zhang Q, Tse G. Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning. BMC Endocr Disord 2021;21:94. [PMID: 33947391 DOI: 10.1186/s12902-021-00751-4] [Cited by in Crossref: 17] [Cited by in F6Publishing: 12] [Article Influence: 17.0] [Reference Citation Analysis]
9 Hamamoto R, Takasawa K, Machino H, Kobayashi K, Takahashi S, Bolatkan A, Shinkai N, Sakai A, Aoyama R, Yamada M, Asada K, Komatsu M, Okamoto K, Kameoka H, Kaneko S. Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. Brief Bioinform 2022:bbac246. [PMID: 35788277 DOI: 10.1093/bib/bbac246] [Reference Citation Analysis]