Applying machine learning techniques to the identification of late-onset hypogonadism in elderly men
Author:
Funder
Ministry of Science and Technology, Taiwan
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
http://link.springer.com/content/pdf/10.1186/s40064-016-2531-8.pdf
Reference27 articles.
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2. Clapauch R, Carmo AM, Marinheiro L, Buksman S, Pessoa I (2008) Laboratory diagnosis of late-onset male hypogonadism andropause. Arq Bras Endocrinol Metabol: ABE&M 52(9):1430–1438
3. Cunningham GR (2006) Testosterone replacement therapy for late-onset hypogonadism. Nat Clin Pract Urol 3(5):260–267
4. Emmelot-Vonk MH, Verhaar HJJ, Nakhai-Pour HR, Grobbee DE, van der Schouw YT (2011) Low testosterone concentrations and the symptoms of testosterone deficiency according to the Androgen Deficiency in Ageing Males (ADAM) and Ageing Males’ Symptoms rating scale (AMS) questionnaires. Clin Endocrinol 74(4):488–494
5. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139
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4. The Epidemiological Characteristics of Late-Onset Hypogonadism in Chinese Middle-Aged and Elderly Men: Two Cross-Sectional Studies in the Same Community;American Journal of Men's Health;2020-11
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