Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Department of Respiratory Medicine in Linköping, Linköping University, Linköping, Sweden.
Int J Chron Obstruct Pulmon Dis. 2023 Jul 18;18:1457-1473. doi: 10.2147/COPD.S412692. eCollection 2023.
In this article, we explore to what extent it is possible to leverage on very small data to build machine learning (ML) models that predict acute exacerbations of chronic obstructive pulmonary disease (AECOPD).
We build ML models using the small data collected during the eHealth Diary telemonitoring study between 2013 and 2017 in Sweden. This data refers to a group of multimorbid patients, namely 18 patients with chronic obstructive pulmonary disease (COPD) as the major reason behind previous hospitalisations. The telemonitoring was supervised by a specialised hospital-based home care (HBHC) unit, which also was responsible for the medical actions needed.
We implement two different ML approaches, one based on time-dependent covariates and the other one based on time-independent covariates. We compare the first approach with standard COX Proportional Hazards (CPH). For the second one, we use different proportions of synthetic data to build models and then evaluate the best model against authentic data.
To the best of our knowledge, the present ML study shows for the first time that the most important variable for an increased risk of future AECOPDs is "maintenance medication changes by HBHC". This finding is clinically relevant since a sub-optimal maintenance treatment, requiring medication changes, puts the patient in risk for future AECOPDs.
The experiments return useful insights about the use of small data for ML.
本文探讨了在多大程度上可以利用非常小的数据来构建机器学习 (ML) 模型，以预测慢性阻塞性肺疾病 (COPD) 急性加重 (AECOPD)。
我们使用 2013 年至 2017 年在瑞典进行的电子健康日记远程监测研究中收集的小数据来构建 ML 模型。这些数据涉及一组患有多种疾病的患者，即 18 名 COPD 患者，这是他们之前住院的主要原因。远程监测由专门的医院家庭护理 (HBHC) 部门监督，该部门还负责所需的医疗行动。
我们实施了两种不同的 ML 方法，一种基于时变协变量，另一种基于时不变协变量。我们将第一种方法与标准 COX 比例风险 (CPH) 进行比较。对于第二种方法，我们使用不同比例的合成数据来构建模型，然后使用真实数据评估最佳模型。
据我们所知，这项 ML 研究首次表明，未来发生 AECOPD 的风险增加的最重要变量是“HBHC 进行的维持性药物变化”。这一发现具有临床意义，因为维持性治疗不理想，需要药物改变，会使患者面临未来发生 AECOPD 的风险。
这些实验为利用小数据进行 ML 提供了有用的见解。
