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Updated: Feb 6, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

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开发基于机器学习的非侵入性诊断模型,用于中国患者的异常性膜性病

Qian Wang1, Wang Xiaolong1, Shibin Su2

  • 1Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Nephrology Institute of the Chinese People's Liberation Army, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing 100853, China.

Clinica chimica acta; international journal of clinical chemistry
|February 4, 2026
PubMed
概括

一个新的CatBoost模型准确地诊断出异常性膜性脏病 (IMN) 的非侵入性. 这种机器学习方法优于IMN诊断的抗脂酶A2受体抗体 (抗PLA2R-Ab) 测试,特别是当活检是不可行的.

关键词:
抗脂酶A2受体抗体 (抗PLA2R-ab) 是一种抗体.在 CatBoost 中使用 CatBoost.异常发病的膜性脏病 (IMN)机器学习 (ML) 是指机器学习.非侵入性诊断是一种非侵入性诊断.

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