Amyotrophic Lateral Sclerosis is a devastating neurodegenerative disease causing rapid degeneration of motor neurons and usually leading to death by respiratory failure. Since there is no cure, treatment’s goal is to improve symptoms and prolong survival. Non-invasive Ventilation (NIV) is an effective treatment, leading to extended life expectancy and improved quality of life. In this scenario, it is paramount to predict its need in order to allow preventive or timely administration. In this work, we propose to use itemset mining together with sequential pattern mining to unravel disease presentation patterns together with disease progression patterns by analysing, respectively, static data collected at diagnosis and longitudinal data from patient follow-up. The goal is to use these static and temporal patterns as features in prognostic models, enabling to take disease progression into account in predictions and promoting model interpretability. As case study, we predict the need for NIV within 90, 180 and 365 days (short, mid and long-term predictions). The learnt prognostic models are promising. Pattern evaluation through growth rate suggests bulbar function and phrenic nerve response amplitude, additionally to respiratory function, are significant features towards determining patient evolution. This confirms clinical knowledge regarding relevant biomarkers of disease progression towards respiratory insufficiency.

中文翻译：

---

使用疾病进展模式学习预后模型：预测肌萎缩侧索硬化症对无创通气的需求


肌萎缩侧索硬化症是一种毁灭性的神经退行性疾病，会导致运动神经元快速退化，通常会因呼吸衰竭而导致死亡。由于无法治愈，治疗的目标是改善症状并延长生存期。无创通气 (NIV) 是一种有效的治疗方法，可以延长预期寿命并提高生活质量。在这种情况下，预测其需求至关重要，以便进行预防性或及时管理。在这项工作中，我们建议将项目集挖掘与顺序模式挖掘结合使用，通过分别分析诊断时收集的静态数据和患者随访中的纵向数据来阐明疾病表现模式和疾病进展模式。目标是使用这些静态和时间模式作为预后模型中的特征，从而能够在预测中考虑疾病进展并提高模型的可解释性。作为案例研究，我们预测 90、180 和 365 天内 NIV 的需求（短期、中期和长期预测）。学习到的预后模型很有希望。通过生长速率进行的模式评估表明，除了呼吸功能之外，延髓功能和膈神经反应幅度是确定患者进化的重要特征。这证实了有关疾病进展为呼吸功能不全的相关生物标志物的临床知识。