监督学习在预测危险因素诱导下急性呼吸窘迫综合征发生风险中的应用

The application of supervised learning in predicting the occurrence of ARDS induced by risk factors

急性呼吸窘迫综合征(acute respiratory distress syndrome,ARDS)缺乏特异性诊断标准,且诱因复杂,在临床实践中往往难以做到早期识别、及时干预,这就需要一种精确、高效的手段辅助识别其发生。基于大数据的机器学习作为一种可以处理海量数据、高效利用有效知识的学习方法,在众多领域发挥了不同作用,在医学领域的重要性日益凸显。截至目前,在医学领域已有大量的机器学习成功应用的案例,其中监督学习算法凭借其可以预测风险的优势,获得众多研究者青睐。本文旨在阐述机器学习算法中监督学习算法在预测危险因素诱导下ARDS发生风险的临床应用。

Acute respiratory distress syndrome(ARDS)lacks specific diagnostic criteria and possesses complex triggers,which makes it difficult to achieve early identification and timely intervention in clinical practice.We need an accurate and efficient means to assist in identifying its occurrence.Machine learning based on big data as a learning method can process massive amounts of data and efficiently utilize effective knowledge,has played a different role in many fields and is increasingly important in the medical field.Up to now,there have been a large number of successful applications of machine learning in the medical field.Among them,supervised learning algorithm has gained the favor of many researchers by its advantages of predicting risks.This paper aims to illustrate the clinical application of supervised learning algorithm in predicting the ARDS induced by risk factors.