A complex combination of clinical, demographic and lifestyle parameters determines the correct diagnosis and the most effective treatment for asthma and Chronic Obstructive Pulmonary Disease patients. Artificial Intelligence techniques help clinicians in devising the correct diagnosis and designing the most suitable clinical pathway accordingly, tailored to the specific patient conditions. In the case of machine learning (ML) approaches, availability of real-world patient clinical data to train and evaluate the ML pipeline deputed to assist clinicians in their daily practice is crucial. However, it is common practice to exploit either synthetic data sets or heavily preprocessed collections cleaning and merging different data sources. In this paper, we describe an automated ML pipeline designed for a real-world data set including patients from a Dutch primary care service, and provide a performance comparison of different prediction models for (i) assessing various clinical parameters, (ii) designing interventions, and (iii) defining the diagnosis.

中文翻译：

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开发用于哮喘和 COPD 的 ML 管道：荷兰初级保健服务的案例

临床、人口统计学和生活方式参数的复杂组合决定了对哮喘和慢性阻塞性肺病患者的正确诊断和最有效的治疗。人工智能技术可帮助临床医生设计正确的诊断并相应地设计最合适的临床路径，以适应特定的患者状况。在机器学习 (ML) 方法的情况下，提供真实世界的患者临床数据来训练和评估用于协助临床医生日常实践的 ML 管道至关重要。但是，通常的做法是利用合成数据集或经过大量预处理的集合来清理和合并不同的数据源。在本文中，