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

CRII：SCH：迈向智能患者流程管理：实时住院患者住院时间建模和预测

• 批准号: 2246158
• 负责人: Yuxin Wen
• 金额: $ 17.44万
• 依托单位: Chapman University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246158&HistoricalAwards=false
• 关键词: CRII; SCH; Towards; Smart; Patient

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.

病人的住院时间已被用作有效规划和管理医院资源的一个重要标准。长期住院增加了患者在医院感染的风险，扰乱了患者流动和获得高质量医疗服务的机会。因此，期望能够可靠地预测特定患者的住院时间长度的模型，以减轻延长的住院时间并指导个性化决策。然而，住院时间可能会受到多种因素的影响，并且可能会根据不同患者的病情和疾病进展而有所不同。大量临床数据的复杂性和动态性，更不用说医疗保健数据中存在大部分缺失和删失值，这对有效建模和动态预测提出了重大挑战。该项目旨在通过建立一个由先进的统计建模，监控和深度学习技术组成的管道，提供一个集成的解决方案，该管道基于随着时间的推移从异构医疗系统收集的患者信息。该项目的成功将促进从传统的标准驱动的放电调度服务到数据驱动的主动调度范式的转变。该项目的成功将减轻医院在资源分配方面的压力，改善病人流量，更重要的是，改善大流行的准备工作。该项目可以为来自不同背景（包括女性和代表性不足的少数民族）的健康信息学、统计学和人工智能领域的本科生和研究生提供基于研究的跨学科培训机会。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集中在识别强烈影响住院时间的因素，而不是预测住院时间本身。此外，现有的方法未能解决固有的不确定性，并且无法整合不同的数据模式和动态预测。该项目提出了一个个性化的框架，通过集成先进的张量融合和时间到事件建模技术，实现智能患者流管理，最终可以更快地实现健康结果并降低住院费用。本文提出的智能框架将在以下几个方面推动实时数据融合和个性化预后的研究：（1）将数据融合和住院时间预测纳入统一的框架中：（2）实时地进行个性化的住院时间预测;（3）自然地具有将不确定性并入决策过程中以提供自信且智能的调度服务的能力。虽然该方法是为患者住院时间预测而提出的，但它不依赖于任何领域知识和特定疾病的限制性假设，因此可以潜在地应用于广泛的事件预测。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Multi-modal learning for inpatient length of stay prediction

• DOI: 10.1016/j.compbiomed.2024.108121
• 发表时间: 2024-02-20
• 期刊: COMPUTERS IN BIOLOGY AND MEDICINE
• 影响因子: 7.7
• 作者: Chen，Junde;Wen，Yuxin;Moen，Scott
• 通讯作者: Moen，Scott

A deep learning approach for inpatient length of stay and mortality prediction

• DOI: 10.1016/j.jbi.2023.104526
• 发表时间: 2023-10
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Junde Chen;Trudi Di Qi;Jacqueline Vu;Yuxin Wen