Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction are increasingly used. Therefore, understanding risks and benefits of such algorithms is relevant at the bedside. ARDS is a complex and severe lung condition that can be challenging to define precisely due to its multifactorial nature. It often arises as a response to various underlying medical conditions, such as pneumonia, sepsis, or trauma, leading to widespread inflammation in the lungs. ML has shown promising potential in supporting the recognition of ARDS in ICU patients. By analyzing a variety of clinical data, including vital signs, laboratory results, and imaging findings, ML models can identify patterns and risk factors associated with the development of ARDS. This detection and prediction could be crucial for timely interventions, diagnosis and treatment. In summary, leveraging ML for the early prediction and detection of ARDS in ICU patients holds great potential to enhance patient care, improve outcomes, and contribute to the evolving landscape of precision medicine in critical care settings. This article is a concise definitive review on artificial intelligence and ML tools for the prediction and detection of ARDS in critically ill patients.

中文翻译：

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用于急性呼吸窘迫综合征检测和预测的机器学习工具。


用于急性呼吸窘迫综合征 (ARDS) 检测和预测的机器学习 (ML) 工具越来越多地使用。因此，了解此类算法的风险和好处在临床上是相关的。 ARDS 是一种复杂而严重的肺部疾病，由于其多因素性质，精确定义可能具有挑战性。它通常是对各种潜在疾病的反应，例如肺炎、败血症或创伤，导致肺部广泛炎症。机器学习在支持 ICU 患者的 ARDS 识别方面显示出巨大的潜力。通过分析各种临床数据，包括生命体征、实验室结果和影像学结果，ML 模型可以识别与 ARDS 发展相关的模式和风险因素。这种检测和预测对于及时干预、诊断和治疗至关重要。总之，利用机器学习对 ICU 患者的 ARDS 进行早期预测和检测具有巨大的潜力，可以加强患者护理、改善结果，并为重症监护环境中精准医疗的不断发展做出贡献。本文是对用于预测和检测危重患者 ARDS 的人工智能和机器学习工具的简明权威综述。