英国Aberystwyth（亚伯）大学团队发表在PLOS Digital Health上的论文“A systematic review of the prediction of hospital length of stay: Towards a unified framework”在Reddit上得到较多关注。

摘要

Hospital length of stay of patients is a crucial factor for the effective planning and management of hospital resources. There is considerable interest in predicting the LoS of patients in order to improve patient care, control hospital costs and increase service efficiency. This paper presents an extensive review of the literature, examining the approaches employed for the prediction of LoS in terms of their merits and shortcomings. In order to address some of these problems, a unified framework is proposed to better generalise the approaches that are being used to predict length of stay. This includes the investigation of the types of routinely collected data used in the problem as well as recommendations to ensure robust and meaningful knowledge modelling. This unified common framework enables the direct comparison of results between length of stay prediction approaches and will ensure that such approaches can be used across several hospital environments. A literature search was conducted in PubMed, Google Scholar and Web of Science from 1970 until 2019 to identify LoS surveys which review the literature. 32 Surveys were identified, from these 32 surveys, 220 papers were manually identified to be relevant to LoS prediction. After removing duplicates, and exploring the reference list of studies included for review, 93 studies remained. Despite the continuing efforts to predict and reduce the LoS of patients, current research in this domain remains ad-hoc; as such, the model tuning and data preprocessing steps are too specific and result in a large proportion of the current prediction mechanisms being restricted to the hospital that they were employed in. Adopting a unified framework for the prediction of LoS could yield a more reliable estimate of the LoS as a unified framework enables the direct comparison of length of stay methods. Additional research is also required to explore novel methods such as fuzzy systems which could build upon the success of current models as well as further exploration of black-box approaches and model interpretability.

患者住院时间是有效规划和管理医院资源的关键因素。为了改善患者护理、控制医院成本和提高服务效率，人们对预测患者的 LoS 非常感兴趣。本文对文献进行了广泛的回顾，检查了用于预测 LoS 的方法的优点和缺点。为了解决其中一些问题，提出了一个统一的框架来更好地概括用于预测停留时间的方法。这包括调查问题中使用的常规收集数据的类型以及确保稳健和有意义的知识建模的建议。这种统一的通用框架能够直接比较住院时间预测方法之间的结果，并将确保这些方法可以在多个医院环境中使用。从 1970 年到 2019 年，在 PubMed、Google Scholar 和 Web of Science 中进行了文献检索，以确定审查文献的 LoS 调查。确定了 32 项调查，从这 32 项调查中，人工确定了 220 篇与 LoS 预测相关的论文。在删除重复项并探索纳入审查的研究参考列表后，剩下 93 项研究。尽管不断努力预测和减少患者的 LoS，但目前该领域的研究仍然是临时性的；因此，模型调整和数据预处理步骤过于具体，导致目前的大部分预测机制仅限于它们所在的医院。采用统一的 LoS 预测框架可以产生更可靠的估计将 LoS 作为一个统一的框架，可以直接比较停留时间的方法。还需要进一步的研究来探索新的方法，例如模糊系统，这些方法可以建立在当前模型的成功基础上，以及对黑盒方法和模型可解释性的进一步探索。（Google Translate结果）

结论

The ability to predict LoS can provide a clinical indicator of the health status of a patient as well as assist in predicting the level of care that is required. It also aids hospital staff with improved prediction of bed and ward utilisation. LoS varies with respect to many factors including severity of illness, diagnosis and a variety of patient factors. This paper provides a review of LoS prediction methods, their respective shortcomings as well as the types of data and features that have been used in the literature. Despite the continuing efforts to predict and reduce the LoS of patients, current research in this domain remains ad-hoc; as such, the model tuning and data preprocessing steps are too specific and result in a large proportion of the current prediction mechanisms being restricted to the hospital that they were employed in. Additionally, several studies focus on hospitals which are contained within very densely populated areas.

Adopting a unified framework for the prediction of LoS could yield a more reliable estimate of the LoS; this can be used across different hospitals where patient populations will be homogeneous with similar demographics and comorbidities amongst patients. Expanding the influence of the models that are generated as part of a unified framework would ensure that the prediction approaches in place are suitably robust as the datasets used would be considerably larger. Increasing the size and coverage of these datasets would improve the ability to detect complex patterns in LoS which could lead to a reduction in prolonged patient LoS where patients have a higher risk of exposure to adverse effects such as hospital acquired infections. Ultimately, the inherent complexity and uncertainty of healthcare systems combined with the vast amounts of electronic healthcare data currently being collected, necessitates prediction methods which are broadly applicable and are capable of modelling uncertainty.

Further research is required to explore novel methods such as fuzzy systems which could build upon the success of current models as well as further exploration of black-box approaches and model interpretability. This is important, as in general, healthcare workers are overwhelmed by the sheer number of patients that they are required to care for, the associated tasks required of them and the amount of data generated by the patients. Machine learning implementations and their explanations, if not sufficiently interpretable, could further hamper the day-to-day effort, of a healthcare worker. Balancing the interpretability of such models with the overall prediction performance that they provide will be a key challenge in the future of LoS prediction.

预测 LoS 的能力可以提供患者健康状况的临床指标，并有助于预测所需的护理水平。它还有助于医院工作人员改进对床位和病房利用率的预测。 LoS 因许多因素而异，包括疾病的严重程度、诊断和各种患者因素。本文回顾了 LoS 预测方法、它们各自的缺点以及文献中使用的数据类型和特征。尽管不断努力预测和减少患者的 LoS，但目前该领域的研究仍然是临时性的；因此，模型调整和数据预处理步骤过于具体，导致目前的大部分预测机制仅限于他们所在的医院。此外，一些研究集中在人口稠密地区的医院。

采用统一的 LoS 预测框架可以产生更可靠的估计；这可以在不同的医院中使用，在这些医院中，患者群体将是同质的，患者之间的人口统计和合并症相似。扩大作为统一框架的一部分生成的模型的影响将确保现有的预测方法具有适当的鲁棒性，因为使用的数据集会大得多。增加这些数据集的大小和覆盖范围将提高检测 LoS 中复杂模式的能力，这可能患者 LoS 延长的情况减少，而在这种情况下患者暴露于医院获得性感染等不良反应的风险更高。最终，医疗保健系统固有的复杂性和不确定性，加上目前正在收集的大量电子医疗保健数据，需要广泛适用且能够对不确定性进行建模的预测方法。

需要进一步的研究来探索新的方法，例如可以建立在当前模型成功基础上的模糊系统，以及进一步探索黑盒方法和模型可解释性。这一点很重要，因为一般来说，医护人员被他们需要照顾的患者数量、所需的相关任务以及患者生成的数据量所淹没。机器学习实现及其解释，如果不能充分解释，可能会进一步阻碍医护人员的日常工作。平衡此类模型的可解释性与它们提供的整体预测性能将是未来 LoS 预测的一个关键挑战。（基于Google Translate结果略加修改）

 

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