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Hypertension and Volume Management

Text mining of hypertension researches in the west Asia region: a 12-year trend analysis

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Article: 2337285 | Received 08 Jan 2024, Accepted 27 Mar 2024, Published online: 14 Apr 2024

References

  • Mauer N, Geldsetzer P, Manne-Goehler J, et al. Longitudinal evidence on treatment discontinuation, adherence, and loss of hypertension control in four middle-income countries. Sci Transl Med. 2022;14(652):1. doi: 10.1126/scitranslmed.abi9522.
  • Abduboriyevna RK, Yusufjonovich NS. Stroke burden in Asia: to the epidemiology in Uzbekistan. Eur Sci Rev. 2018;7:156–15.
  • Johnson W, Onuma O, Owolabi M, et al. Stroke: a global response is needed [internet]. Bull World Health Organ. 2016;94(9):634–634A. doi: 10.2471/BLT.16.181636.
  • Turana Y, Tengkawan J, Chia YC, et al. Hypertension and stroke in Asia: a comprehensive review from HOPE Asia. J Clin Hypertens (Greenwich). 2021;23(3):513–521. doi: 10.1111/jch.14099.
  • Toyoda K. Intensive blood pressure lowering for ischemic stroke patients: does it prevent ischemia or bleeding? Hypertens Res. 2022;45(5):769–771. doi: 10.1038/s41440-022-00892-6.
  • Kitagawa K. Blood pressure management for secondary stroke prevention. Hypertens Res. 2022;45(6):936–943. doi: 10.1038/s41440-022-00908-1.
  • Hägg-Holmberg S, Dahlström EH, Forsblom CM, et al. The role of blood pressure in risk of ischemic and hemorrhagic stroke in type 1 diabetes. Cardiovasc Diabetol. 2019;18(1):88. doi: 10.1186/s12933-019-0891-4.
  • Feigin VL, Stark BA, Johnson CO, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet Neurol. 2021;20(10):795–820. doi: 10.1016/S1474-4422(21)00252-0.
  • Zhou B, Carrillo-Larco RM, Danaei G, et.al. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet. 2021;398(10304):957–980. doi: 10.1016/S0140-6736(21)01330-1.
  • Sepehri MM, Khavaninzadeh M, Rezapour M, et al. A data mining approach to fistula surgery failure analysis in hemodialysis patients. 2011 18th Iranian Conference of Biomedical Engineering (ICBME); 2011 Dec. 14; Iran. Piscataway (NJ): IEEE. p. 15–20. doi: 10.1109/ICBME.2011.6168546.
  • Rezapour M, Nakhostin Ansari N, Khavanin Zadeh M, et al. Risk of stroke in hypertensive diabetic chronic kidney disease patients after Central venous catheter placement. Razi J Med Sci. 2020;27(8):10–21.
  • Samizadeh R, Zadeh MK, Jadidi M, et al. Discovery of dangerous self-medication methods with patients, by using social network mining. IJBIDM. 2023;23(3):277–287. doi: 10.1504/IJBIDM.2023.133186.
  • Rezapour M, Nakhostin Ansari N. Incidence of stroke in hemodialysis patients with Central venous catheter: a systematic review. J Vessels Circulat. 2021;2(1):27.
  • Rezapour M, Asadi R, Marghoob B. Machine learning algorithms as new screening framework for recommendation of appropriate vascular access and stroke reduction. Int J Hosp Res. 2021;10(3):4–7. Available from: http://ijhr.iums.ac.ir/article_126609.html
  • Rezapour M, Nakhostin Ansari N. Producing a telerehabilitation mobile application and a web-based smart dashboard platform for online monitoring patients with a history of stroke during covid-19 pandemic and Post-Pandemic era. Int J Basic Sci Med. 2021;6(4):127–131. doi: 10.34172/ijbsm.2021.23.
  • Rezapour M, Sepehri MM, Zadeh MK, et al. A new method to determine anastomosis angle configuration for arteriovenous fistula maturation. Med J Islam Repub Iran. 2018;32(1):62–370. doi: 10.14196/mjiri.32.62.
  • Rezapour M, Payani E, Taran M, et al. Roles of triglyceride and phosphate in atherosclerosis of diabetic hemodialysis patients. Med J Islam Republic Iran. 2017;31(1):465–471. doi: 10.14196/mjiri.31.80.
  • Khavanin Zadeh M, Rezapour M, Sepehri MM. Data mining performance in identifying the risk factors of early arteriovenous fistula failure in hemodialysis patients. Int J Hosp Res. 2013;2(1):49–54.
  • Rezapour M, Khavanin Zadeh M, Sepehri MM. Implementation of predictive data mining techniques for identifying risk factors of early AVF failure in hemodialysis patients. Comput Math Methods Med. 2013;2013:830745–830748. doi: 10.1155/2013/830745.
  • Rezapour M, Taran S, Parast MB, et al. The impact of vascular diameter ratio on hemodialysis maturation time: evidence from data mining approaches and thermodynamics law. Med J Islamic Republic Iran. 2016;30:359.
  • Rezapour M, Khavaninzadeh M. Association between non-matured arterio-venus fistula and blood pressure in hemodialysis patients. Med J Islamic Republic Iran. 2014;28:144.
  • Rezapour M, Zadeh MK, Sepehri MM, et al. Less primary fistula failure in hypertensive patients. J Hum Hypertens. 2018;32(4):311–318. doi: 10.1038/s41371-018-0052-3.
  • Rüdiger M, Antons D, Joshi AM, et al. Topic modeling revisited: new evidence on algorithm performance and quality metrics. PLoS One. 2022;17(4):e0266325. doi: 10.1371/journal.pone.0266325.
  • Wang SH, Ding Y, Zhao W, et al. Text mining for identifying topics in the literatures about adolescent substance use and depression. BMC Public Health. 2016;16(1):279. doi: 10.1186/s12889-016-2932-1.
  • Liu Y, Du F, Sun J, et al. iLDA: an interactive latent Dirichlet allocation model to improve topic quality. J Inform Sci. 2020;46(1):23–40. doi: 10.1177/0165551518822455.
  • Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.
  • Griffiths TL, Steyvers M. Finding scientific topics. Proc Natl Acad Sci USA. 2004;101(1):5228–5235. doi: 10.1073/pnas.0307752101.
  • Blei DM. Probabilistic topic models. Commun ACM. 2012;55(4):77–84. doi: 10.1145/2133806.2133826.
  • Ritter A, Etzioni O. A latent Dirichlet allocation method for selectional preferences. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics; 2010 Jul. Uppsala (Sweden): SIG. p. 424–434.
  • Guo Y, Barnes SJ, Jia Q. Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent Dirichlet allocation. Tour. Manag. 2017;59:467–483 doi: 10.1016/j.tourman.2016.09.009.
  • Albalawi R, Yeap TH, Benyoucef M. Using topic modeling methods for short-text data: a comparative analysis. Front Artif Intell. 2020;3:42. doi: 10.3389/frai.2020.00042.
  • Obadimu A, Mead E, Agarwal N. Identifying latent toxic features on YouTube using non-negative matrix factorization. The Ninth International Conference on Social Media Technologies, Communication, and Informatics; 2019 November 28; Valencia, Spain. IEEE; p. 1–6.
  • Sánchez-Franco MJ, Rey-Moreno M. Do travelers’ reviews depend on the destination? An analysis in coastal and urban peer-to-peer lodgings. Psychol Market. 2022;39(2):441–459. doi: 10.1002/mar.21608.
  • Egger R. 2022). Topic modelling. Modelling hidden semantic structures in textual data. In Egger R, editor. Applied data science in tourism. Interdisciplinary approaches, methodologies and applications. Berlin, Germany: Springer; p. 18.
  • Egger R, Yu J. A topic modeling comparison between LDA, NMF, top2vec, and bertopic to demystify Twitter posts. Front Sociol. 2022;7:886498. doi: 10.3389/fsoc.2022.886498.
  • Blair SJ, Bi Y, Mulvenna MD. Aggregated topic models for increasing social media topic coherence. Appl Intell. 2020;50(1):138–156. doi: 10.1007/s10489-019-01438-z.
  • Chen Y, Zhang H, Liu R, et al. Experimental explorations on short text topic mining between LDA and NMF based schemes. Knowl Based Syst. 2019;163:1–13. doi: 10.1016/j.knosys.2018.08.011.
  • Wang J, Zhang XL. Deep NMF topic modeling. 2021. [accessed January 18, 2022]. Available from: http://arxiv.org/pdf/2102.12998v1
  • Zhang Y, Chen H, Lu J, et al. Detecting and predicting the topic change of knowledge-based systems: a topic-based bibliometric analysis from 1991 to 2016. Knowledge-Based Syst. 2017;133:255–268. doi: 10.1016/j.knosys.2017.07.011.
  • Roustaei M, Nikmaneshi MR, Firoozabadi B. Simulation of low density lipoprotein (LDL) permeation into multilayer coronary arterial wall: interactive effects of wall shear stress and fluid-structure interaction in hypertension. J Biomech. 2018;67:114–122. doi: 10.1016/j.jbiomech.2017.11.029.
  • Paszkowiak JJ, Dardik A. Arterial wall shear stress: observations from the bench to the bedside. Vasc Endovascular Surg. 2003;37(1):47–57. doi: 10.1177/153857440303700107.
  • Samady H, Eshtehardi P, McDaniel MC. Coronary artery wall shear stress is associated with progression and transformation of atherosclerotic plaque and arterial remodeling in patients with coronary artery disease. Circulation. 2011;124(7):779–788. doi: 10.1161/CIRCULATIONAHA.111.021824.
  • Zhou M, Yu Y, Chen R. Wall shear stress and its role in atherosclerosis. Front Cardiovasc Med. 2023;10:1083547. doi: 10.3389/fcvm.2023.1083547.
  • Rezazadeh M, Ostadi R. Numerical simulation of the wall shear stress distribution in a carotid artery bifurcation. J Mech Sci Technol. 2022;36(10):5035–5046. doi: 10.1007/s12206-022-0917-9.
  • Shimokawa H. Rho-kinase as a novel therapeutic target in treatment of cardiovascular diseases. J Cardiovasc Pharmacol. 2002;39(3):319–327. doi: 10.1097/00005344-200203000-00001.
  • Ocaranza MP, Jalil JE. Mitogen-activated protein kinases as biomarkers of hypertension or cardiac pressure overload. Hypertension. 2010;55(1):23–25. doi: 10.1161/HYPERTENSIONAHA.109.141960.
  • Abedi F, Omidkhoda N, Arasteh O, et al. The therapeutic role of rho kinase inhibitor, fasudil, on pulmonary hypertension; a systematic review and meta-analysis. Drug Res (Stuttg). 2023;73(1):5–16. doi: 10.1055/a-1879-3111.
  • Katibeh M, Moghaddam A, Yaseri M, et al. Hypertension and associated factors in the Islamic Republic of Iran: a population-based study. East Mediterr Health J. 2020;26(3):304–314.
  • Kim HC, Lee H, Lee HH, et al. Korea hypertension fact sheet 2021: analysis of nationwide population-based data with special focus on hypertension in women. Clin Hypertens. 2022;28(1):1–5. doi: 10.1186/s40885-021-00188-w.
  • Lebedev MA, Opris I, Casanova MF. Augmentation of brain function: facts, fiction and controversy. Front Syst Neurosci. 2018;12:45. doi: 10.3389/fnsys.2018.00045.