Leão Tiago, Madeira Sara C, Gromicho Marta, de Carvalho Mamede, Carvalho Alexandra M
Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
J Biomed Inform. 2021 May;117:103730. doi: 10.1016/j.jbi.2021.103730. Epub 2021 Mar 16.
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease causing patients to quickly lose motor neurons. The disease is characterized by a fast functional impairment and ventilatory decline, leading most patients to die from respiratory failure. To estimate when patients should get ventilatory support, it is helpful to adequately profile the disease progression. For this purpose, we use dynamic Bayesian networks (DBNs), a machine learning model, that graphically represents the conditional dependencies among variables. However, the standard DBN framework only includes dynamic (time-dependent) variables, while most ALS datasets have dynamic and static (time-independent) observations. Therefore, we propose the sdtDBN framework, which learns optimal DBNs with static and dynamic variables. Besides learning DBNs from data, with polynomial-time complexity in the number of variables, the proposed framework enables the user to insert prior knowledge and to make inference in the learned DBNs. We use sdtDBNs to study the progression of 1214 patients from a Portuguese ALS dataset. First, we predict the values of every functional indicator in the patients' consultations, achieving results competitive with state-of-the-art studies. Then, we determine the influence of each variable in patients' decline before and after getting ventilatory support. This insightful information can lead clinicians to pay particular attention to specific variables when evaluating the patients, thus improving prognosis. The case study with ALS shows that sdtDBNs are a promising predictive and descriptive tool, which can also be applied to assess the progression of other diseases, given time-dependent and time-independent clinical observations.
肌萎缩侧索硬化症（ALS）是一种神经退行性疾病，会导致患者迅速丧失运动神经元。该疾病的特征是功能快速受损和通气功能下降，导致大多数患者死于呼吸衰竭。为了估计患者何时应接受通气支持，充分了解疾病进展情况会有所帮助。为此，我们使用动态贝叶斯网络（DBN），这是一种机器学习模型，它以图形方式表示变量之间的条件依赖性。然而，标准的DBN框架仅包括动态（时间相关）变量，而大多数ALS数据集同时包含动态和静态（时间无关）观测值。因此，我们提出了sdtDBN框架，该框架可以学习包含静态和动态变量的最优DBN。除了从数据中学习DBN外，该框架在变量数量上具有多项式时间复杂度，还能让用户插入先验知识并在学习到的DBN中进行推理。我们使用sdtDBN来研究来自葡萄牙ALS数据集的1214名患者的病情进展。首先，我们预测患者会诊中每个功能指标的值，取得了与现有最先进研究相媲美的结果。然后，我们确定每个变量在患者接受通气支持前后病情恶化过程中的影响。这些有洞察力的信息可以引导临床医生在评估患者时特别关注特定变量，从而改善预后。ALS的案例研究表明，sdtDBN是一种很有前景的预测和描述工具，鉴于存在时间相关和时间无关的临床观测值，它也可应用于评估其他疾病的进展情况。
