Machine learning-based reproducible prediction of type 2 diabetes subtypes

Aims/hypothesis Clustering-based subclassification of type 2 diabetes, which reflects pathophysiology and genetic predisposition, is a promising approach for providing personalised and effective therapeutic strategies. Ahlqvist’s classification is currently the most vigorously validated method becau...

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Veröffentlicht in:	Diabetologia 2024-11, Vol.67 (11), p.2446-2458
Hauptverfasser:	Tanabe, Hayato, Sato, Masahiro, Miyake, Akimitsu, Shimajiri, Yoshinori, Ojima, Takafumi, Narita, Akira, Saito, Haruka, Tanaka, Kenichi, Masuzaki, Hiroaki, Kazama, Junichiro J., Katagiri, Hideki, Tamiya, Gen, Kawakami, Eiryo, Shimabukuro, Michio
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Age Aged Algorithms Classification Clinical medicine Clinical outcomes Cohort analysis Cohort Studies Diabetes Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - diagnosis Diabetic retinopathy Female Glycated Hemoglobin - metabolism Health risks Human Physiology Humans Insulin Insulin Resistance - physiology Internal Medicine Kidney diseases Learning algorithms Machine Learning Male Medical diagnosis Medical treatment Medicine Medicine & Public Health Metabolic Diseases Middle Aged Pathophysiology Population studies Predictions Retinopathy
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