Chronic respiratory diseases, such as chronic obstructive pulmonary disease and asthma, are a serious health crisis, affecting a large number of people globally and inflicting major costs on the economy. Current methods for assessing the progression of respiratory symptoms are either subjective and inaccurate, or complex and cumbersome, and do not incorporate environmental factors. Lacking predictive assessments and early intervention, unexpected exacerbations can lead to hospitalizations and high medical costs. This work presents a multi-modal solution for predicting the exacerbation risks of respiratory diseases, such as COPD, based on a novel spatio-temporal machine learning architecture for real-time and accurate respiratory events detection, and tracking of local environmental and meteorological data and trends. The proposed new machine learning architecture blends key attributes of both convolutional and recurrent neural networks, allowing extraction of both spatial and temporal features encoded in respiratory sounds, thereby leading to accurate classification and tracking of symptoms. Combined with the data from environmental and meteorological sensors, and a predictive model based on retrospective medical studies, this solution can assess and provide early warnings of respiratory disease exacerbations. This research will improve the quality of patients' lives through early medical intervention, thereby reducing hospitalization rates and medical costs.

中文翻译：

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基于时空机器学习架构的多模式呼吸系统疾病加重预测技术

慢性呼吸系统疾病，例如慢性阻塞性肺疾病和哮喘，是严重的健康危机，在全球范围内影响着许多人，给经济造成重大损失。当前评估呼吸道症状进展的方法是主观的，不准确的，复杂的和繁琐的，并且没有纳入环境因素。缺乏预测性评估和早期干预，意料之外的病情加重可能导致住院和高昂的医疗费用。这项工作基于一种新颖的时空机器学习架构，可实时，准确地检测呼吸事件，并跟踪本地环境和气象数据，并为预测呼吸道疾病（例如COPD）的恶化风险提供了一种多模式解决方案。趋势。拟议的新型机器学习架构融合了卷积神经网络和循环神经网络的关键属性，可以提取呼吸声中编码的时空特征，从而实现对症状的准确分类和跟踪。结合来自环境和气象传感器的数据以及基于回顾性医学研究的预测模型，该解决方案可以评估并提供呼吸道疾病加重的早期预警。这项研究将通过早期医疗干预改善患者的生活质量，从而降低住院率和医疗费用。从而导致对症状的准确分类和跟踪。结合来自环境和气象传感器的数据以及基于回顾性医学研究的预测模型，该解决方案可以评估并提供呼吸道疾病加重的早期预警。这项研究将通过早期医疗干预改善患者的生活质量，从而降低住院率和医疗费用。从而导致对症状的准确分类和跟踪。结合来自环境和气象传感器的数据以及基于回顾性医学研究的预测模型，该解决方案可以评估并提供呼吸道疾病加重的早期预警。这项研究将通过早期医疗干预改善患者的生活质量，从而降低住院率和医疗费用。