Inpatient length of stay (LoS) is an important managerial metric which if known in advance can be used to efficiently plan admissions, allocate resources and improve care. Using historical patient data and machine learning techniques, LoS prediction models can be developed. Ethically, these models can not be used for patient discharge in lieu of unit heads but are of utmost necessity for hospital management systems in charge of effective hospital planning. Therefore, the design of the prediction system should be adapted to work in a true hospital setting. In this study, we predict early hospital LoS at the granular level of admission units by applying domain adaptation to leverage information learned from a potential source domain. Time-varying data from 110,079 and 60,492 patient stays to 8 and 9 intensive care units were respectively extracted from eICU-CRD and MIMIC-IV. These were fed into a Long-Short Term Memory and a Fully connected network to train a source domain model, the weights of which were transferred either partially or fully to initiate training in target domains. Shapley Additive exPlanations (SHAP) algorithms were used to study the effect of weight transfer on model explanability. Compared to the benchmark, the proposed weight transfer model showed statistically significant gains in prediction accuracy (between 1% and 5%) as well as computation time (up to 2hrs) for some target domains. The proposed method thus provides an adapted clinical decision support system for hospital management that can ease processes of data access via ethical committee, computation infrastructures and time.

翻译：住院时长（LoS）是重要的管理指标，若提前获知，可用于高效规划入院、资源分配及改善护理服务。利用历史患者数据与机器学习技术，可构建住院时长预测模型。从伦理角度而言，此类模型不能替代病房负责人进行患者出院决策，但对负责医院有效规划的管理系统至关重要。因此，预测系统的设计需适配真实医院环境。本研究通过应用领域自适应技术，利用潜在源域中学习到的知识，在入院单元粒度上实现早期住院时长预测。我们分别从eICU-CRD和MIMIC-IV数据库中提取了110,079例和60,492例患者入住8个和9个重症监护单元的时变数据，将其输入长短期记忆网络与全连接网络以训练源域模型，并将模型权重部分或全部迁移至目标域以初始化训练。采用沙普利加性解释（SHAP）算法分析权重迁移对模型可解释性的影响。与基准模型相比，所提出的权重迁移模型在部分目标域的预测准确率（提升1%-5%）和计算时间（最多缩短2小时）上均表现出统计显著性增益。该方法为医院管理提供了适配的临床决策支持系统，可简化伦理委员会数据访问、计算基础设施及时间等流程。