Torres Romina, Zurita Christopher, Mellado Diego, Nicolis Orietta, Saavedra Carolina, Tuesta Marcelo, Salinas Matías, Bertini Ayleen, Pedemonte Oneglio, Querales Marvin, Salas Rodrigo
Faculty of Engineering, Universidad Andres Bello, Viña del Mar 2531015, Chile.
Millennium Institute for Intelligent Healthcare Engineering (iHealth), Santiago 7820436, Chile.
Diagnostics (Basel). 2023 Jan 30;13(3):508. doi: 10.3390/diagnostics13030508.
Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of 0.03±0.013 and an R2 of 63±19%, where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an R2 up to 92%. The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase.
心血管疾病是全球主要的死亡原因。因此，心血管康复项目对于减少每年由这种疾病导致的死亡至关重要，主要针对冠心病患者。新冠疫情不仅给这一领域带来了挑战，也是开启这些项目远程或混合版本的契机，这有可能减少因地理/时间障碍而退出康复项目的患者数量。本文提出了一种使用堆叠机器学习、迁移特征学习和联合分布适应工具，利用具有不同特征的回顾性和前瞻性数据构建心血管康复预测模型的方法，以解决这一问题。我们在智利的一个康复中心对该方法进行了说明，在那里，10折交叉验证获得的预测性能结果达到了误差水平，归一化均方误差（NMSE）为0.03±0.013，决定系数（R2）为63±19%，其中最佳性能的误差水平为归一化均方误差0.008，R²高达92%。这些结果对于远程心血管康复项目来说是令人鼓舞的，因为这些模型可以支持对在当前康复阶段需要更多帮助才能成功的远程患者进行优先级排序。
