Birlamchi tibbiy yordam muassasalarida yuqori xavfli gipertenziv inqirozlarni qisqa muddatli bashorat qilish uchun sun'iy intellekt modellarini ishlab chiqish

Mualliflar

  • Inomov Kamoliddin Mamasoli o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Tibbiyot kafedrasi
  • Xoshimov Muslimbek Toxirjon o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Umumiy kasbiy fanlar kafedrasi
  • Karimjonov Jaloliddin Abdusattor o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Umumiy kasbiy fanlar kafedrasi
  • Soliyev Muhammadqodir Abdugaffor o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Tibbiyot kafedrasi
  • Nosirov Temurbek Isroiljon o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Tibbiyot kafedrasi
  • Khamrayev Abdurahmon Umar o’g’li Biznes va fanlar universiteti, Tibbiyot fakulteti, Tibbiyot kafedrasi

Kalit so‘zlar:

Sun’iy intellekt, Gipertenziv kriz, Mashinali o‘qitish, Birlamchi tibbiy yordam, Xavfni prognozlash, Kardiologiya.

Abstrak

Gipertenziv kriz yurak-qon tomir kasalliklari asoratlarining asosiy sabablaridan biri bo‘lib qolmoqda. An’anaviy xavf shkalalari uzoq muddatli oqibatlarga qaratilgan bo‘lib, birlamchi bo‘g‘inda o‘tkir holatlarni prognoz qila olmaydi.  Maqsad: Gipertenziv krizlarni qisqa muddatli (7 kunlik) prognoz qilish uchun Mashinali O‘qitish (Machine Learning) modelini ishlab chiqish va validatsiya qilish. Usullar: Essensial gipertenziyasi bo‘lgan 50 nafar bemorning elektron tibbiy ma’lumotlari asosida retrospektiv kogorta tadqiqoti o‘tkazildi. Uch xil algoritm (Logistik regressiya, Random Forest, XGBoost) o‘zaro taqqoslandi. Asosiy omillar sifatida gemodinamik o‘zgaruvchanlik, biokimyoviy markerlar va dori ichish tartibiga rioya qilish (komplayens) olindi.

References

World Health Organization (WHO). Hypertension: Key Facts. WHO, Geneva, 2023. Available at: https://www.who.int/news-room/fact-sheets/detail/hypertension

Whelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults. Hypertension. 2018;71(6):e13-e115.

Visseren FLJ, Mach F, Smulders YM, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J. 2021;42(34):3227-3337.

Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017;69(21):2657-2664.

Johnson KW, Glicksberg BS, Johnson NO, et al. Automated machine learning to predict the progression of arterial hypertension. AMIA Annu Symp Proc. 2018;2018:1342-1351.

Varounis C, Katsi V, Nihoyannopoulos P, Lekakis J, Tousoulis D. Cardiovascular Hypertensive Crisis: Recent Evidence and Review of the Literature. Front Cardiovasc Med. 2017;3:51.

Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016:785-794.

Parati G, Stergiou GS, Dolan E, Bilo G. Blood pressure variability: clinical relevance and application. J Clin Hypertens (Greenwich). 2018;20(7):1133-1137.

Stevens SL, Wood S, Koshiaris C, et al. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ. 2016;354:i4098.

Burnier M, Egan BM. Adherence in Hypertension: A Review of Prevalence, Risk Factors, Impact, and Management. Circ Res. 2019;124(7):1124-1140.

Nashr qilingan

2026-01-21

Nashr

Jild 3 Nomeri 1 (2026): UNIVERSAL XALQARO ILMIY JURNAL

Bo'lim

Maqolalar

How to Cite

Birlamchi tibbiy yordam muassasalarida yuqori xavfli gipertenziv inqirozlarni qisqa muddatli bashorat qilish uchun sun’iy intellekt modellarini ishlab chiqish. (2026). Universal Xalqaro Ilmiy Jurnal, 3(1), 139-144. https://universaljurnal.uz/index.php/jurnal/article/view/3883