CRII: SCH: Towards Smart Patient Flow Management: Real-time Inpatient Length of Stay Modeling and Prediction

病人的住院时间已被用作有效规划和管理医院资源的基本标准。延长住院时间增加了患者在医院获得性感染的风险，并扰乱了患者流动和获得高质量医疗保健服务的机会。因此，一个能够可靠地预测特定患者住院时间的模型对于减轻延长的住院时间和指导个性化决策是可取的。然而，住院时间长短可能受到多种因素的影响，并可能根据不同患者的病情和疾病进展而变化。大量临床数据的复杂性和动态性，更不用说医疗数据中存在的大量缺失和删减值，为高效建模和动态预测带来了重大挑战。该项目旨在通过建立一个由先进的统计建模、监测和深度学习技术组成的管道，提供一个集成的解决方案，该管道基于从异构医疗系统中收集的患者信息。该项目的成功将促进从传统的标准驱动的出院调度服务向数据驱动的主动调度模式的转变。该项目的成功将减轻医院在资源分配方面的压力，改善病人流动，更重要的是，改善大流行病的防范工作。该项目可为来自不同背景的本科生和研究生，包括妇女和代表性不足的少数群体，提供基于研究的跨学科卫生信息学、统计学和人工智能培训机会。该项目将解决医疗数据分析的关键挑战，即异质性、多模态和数据稀疏性。传统的数据驱动方法主要侧重于确定强烈影响逗留时间长短的因素，而不是预测逗留时间本身。此外，现有方法未能解决固有的不确定性，无法结合不同的数据模式和动态预测。该项目提出了一个个性化框架，将先进的张量融合和时间到事件建模技术集成到智能患者流程管理中，最终可以更快地实现健康结果并降低住院费用。提出的智能框架将在以下几个方面推进当前实时数据融合和个性化预测的研究现状：(1)将数据融合和住院时间预测整合到一个统一的框架中；(2)实时实现个性化的入住时间预测；(3)具有将不确定性纳入决策过程的能力，以提供自信和智能的调度服务。虽然该方法是为患者住院时间预测而提出的，但它不依赖于领域知识和特定疾病的任何限制性假设，因此可以潜在地应用于广泛的事件预测。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Patient length of stay has been used as an essential criterion for the effective planning and management of hospital resources. Prolonged stay increases patients’ risk of hospital-acquired infections and disrupts patient flow and access to high-quality healthcare services. As such, a model that can reliably predict the length of stay for a specific patient is desirable to mitigate the prolonged stay and guide personalized decision-making. However, the length of stay can be affected by a multitude of factors and can vary based on different patients’ conditions and disease progression. The complex and dynamic nature of massive clinical data, not to mention the presence of a large portion of missing and censored values in the healthcare data, poses significant challenges for efficient modeling and dynamic prediction. This project aims to offer an integrated solution by establishing a pipeline consisting of advanced statistical modeling, monitoring, and deep learning techniques based on patient information collected from heterogeneous medical systems over time. The success of the project will catalyze a transition from a traditional standard-driven discharge scheduling service to a data-driven proactive scheduling paradigm. The success of the project will alleviate the hospital’s pressure on resource allocation and improve patient flow and, more importantly, pandemic preparedness. The project can provide opportunities for research-based interdisciplinary training of undergraduate and graduate students in health informatics, statistics, and artificial intelligence from diverse backgrounds, including women and underrepresented minorities.This project will address the critical challenges of healthcare data analysis, i.e., heterogeneity, multi-modality, and data sparsity. Conventional data-driven methods have been predominantly focused on identifying the factors that strongly influence the length of stay as opposed to predicting the length of stay itself. Moreover, the existing approaches failed to address the inherent uncertainty and were incapable of incorporating different data modalities and dynamic prediction. The project proposes a personalized framework by integration of advanced tensor fusion and time-to-event modeling techniques towards smart patient flow management, which ultimately allows for faster achievement of health outcomes and reduction of hospitalized costs. The proposed intelligent framework will advance the state-of-art research of real-time data fusion and personalized prognosis in the following aspects: (1) brings the data fusion and length of stay prediction into a unified framework; (2) facilitates personalized length of stay prediction in a real-time manner; (3) naturally has the capability to incorporate uncertainties in the decision-making process to provide a confident and intelligent scheduling service. Although the methodology is proposed for the patient length of stay prediction, it does not depend on any restrictive assumptions of domain knowledge and specific disease and thus can potentially be applied to a broad range of events predictions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.