The COVID-19 pandemic has highlighted the critical need for advanced technology in healthcare. Clinical Decision Support Systems (CDSS) utilizing Artificial Intelligence (AI) have emerged as one of the most promising technologies for improving patient outcomes. This study’s focus on developing a deep state-space model (DSSM) is of utmost importance, as it addresses the current limitations of AI predictive models in handling high-dimensional and longitudinal electronic health records (EHRs). The DSSM’s ability to capture time-varying information from unstructured medical notes, combined with label-dependent attention for interpretability, will allow for more accurate risk prediction for patients. As we move into a post-COVID-19 era, the importance of CDSS in precision medicine cannot be ignored. This study’s contribution to the development of DSSM for unstructured medical notes has the potential to greatly improve patient care and outcomes in the future.

COVID-19 大流行凸显了医疗保健领域对先进技术的迫切需求。利用人工智能 (AI) 的临床决策支持系统 (CDSS) 已成为改善患者治疗效果最有前途的技术之一。这项研究的重点是开发深度状态空间模型（DSSM），这一点至关重要，因为它解决了人工智能预测模型目前在处理高维和纵向电子健康记录（EHR）方面的局限性。 DSSM 能够从非结构化医疗记录中捕获随时间变化的信息，结合标签相关的可解释性关注，将为患者提供更准确的风险预测。随着我们进入后 COVID-19 时代，CDSS 在精准医疗中的重要性不容忽视。这项研究对非结构化医疗记录 DSSM 的开发做出了贡献，有可能在未来极大地改善患者护理和治疗结果。