Clinician guidelines recommend health management to tailor the form of care to the expected course of diseases. Hence, in order to decide upon a suitable treatment plan, health professionals benefit from decision support, i.e., predictions about how a disease is to evolve. In clinical practice, such a prediction model requires interpretability. Interpretability, however, is often precluded by complex dynamic models that would be capable of capturing the intrapersonal variability of disease trajectories. Therefore, we develop a cross-sectional ARMA model that allows for inference of the expected course of symptoms. Distinct from traditional time series models, it generalizes to cross-sectional settings and thus patient cohorts (i.e., it is estimated to multiple instead of single disease trajectories). Our model is evaluated according to a longitudinal 52-week study involving 928 patients with low back pain. It achieves a favorable prediction performance while maintaining interpretability. In sum, we provide decision support by informing health professionals about whether symptoms will have the tendency to stabilize or continue to be severe.

临床医生指南建议进行健康管理，以针对预期的疾病进程调整护理形式。因此，为了决定合适的治疗计划，卫生专业人员将从决策支持中受益，即对疾病如何发展的预测。在临床实践中，这种预测模型需要可解释性。但是，可解释性通常被能够捕获疾病轨迹的人际变异性的复杂动态模型所排除。因此，我们开发了一个截面ARMA模型，该模型可以推断预期的症状过程。与传统的时间序列模型不同，它适用于横断面设置以及患者队列（即，估计为多个而不是单个疾病轨迹）。我们的模型是根据一项涉及928名腰背痛患者的52周纵向研究评估的。它在保持可解释性的同时实现了良好的预测性能。总而言之，我们通过告知医疗专业人员症状是否会趋于稳定或持续加剧来提供决策支持。