This paper proposes a weakly-supervised machine learning-based approach aiming at a tool to alert patients about possible respiratory diseases. Various types of pathologies may affect the respiratory system, potentially leading to severe diseases and, in certain cases, death. In general, effective prevention practices are considered as major actors towards the improvement of the patient's health condition. The proposed method strives to realize an easily accessible tool for the automatic diagnosis of respiratory diseases. Specifically, the method leverages Variational Autoencoder architectures permitting the usage of training pipelines of limited complexity and relatively small-sized datasets. Importantly, it offers an accuracy of 57 %, which is in line with the existing strongly-supervised approaches.

翻译：本文提出了一种基于弱监督机器学习的方法，旨在开发用于警示患者潜在呼吸系统疾病的工具。多种病理因素可能影响呼吸系统，进而导致严重疾病甚至死亡。通常，有效预防措施被认为是改善患者健康状况的关键手段。该方法致力于实现一种易于获取的呼吸系统疾病自动诊断工具。具体而言，该方法利用变异自编码器架构，能够采用复杂度有限的训练流程和相对小规模的数据集。重要的是，该方法实现了57%的准确率，与现有强监督方法水平相当。