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SUCCESS OF SYMPTOM AND CLINICAL SIGN CLUSTERING BASED ON EXPERIENCE: PROSPECTS IN CLINICAL MEDICINE

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MAQOLA ANNOTATSIYASI

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This article discusses the significance and application of clustering analysis in categorizing symptoms and clinical signs in clinical medicine. The authors present findings from studies conducted based on experience, demonstrating the success of clustering methods in diagnosing and treating various conditions. Through an analysis of the effectiveness and prospects of such methods, the article draws conclusions about their significant contribution to modern clinical medicine.

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Teglar

# analysis# treatment# method# research# diagnosis# contribution# success# conditions

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Maqola idintifikatorlari

Foydalanilgan adabiyotlar

1.Obermeyer Z, Emanuel EJ. "Predicting the Future -Big Data, Machine Learning, and Clinical Medicine." New England Journal of Medicine. 2016;375(13):1216-1219.2.Rajkomar A, Dean J, Kohane I. "Machine Learning in Medicine." New England Journal of Medicine. 2019;380(14):1347-1358.3.Beam AL, Kohane IS. "Big Data and Machine Learning in Health Care." JAMA. 2018;319(13):1317–1318.4.Norgeot B, Glicksberg BS, Butte AJ. "A Call for Deep-Learning Healthcare." Nature Medicine. 2019;25(1):14-15.5.Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. "Artificial Intelligence in Precision Cardiovascular Medicine." Journal of the American College of Cardiology. 2017;69(21):2657-2664.6.Topol EJ. "High-Performance Medicine: The Convergence of Human and Artificial Intelligence." Nature Medicine. 2019;25(1):44-56.7.Johnson KW, Torres Soto J, GlicksbergBS, et al. "Artificial Intelligence in Cardiology." Journal of the American College of Cardiology. 2018;71(23):2668-2679.8.Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. "Ensuring Fairness in Machine Learning to Advance Health Equity." Annals of Internal Medicine. 2018;169(12):866–872.

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