Recent research has highlighted the potential of linking predictive and
prescriptive analytics. However, it remains widely unexplored how both
paradigms could benefit from one another to address today's major challenges in
healthcare. One of these is smarter planning of resource capacities for frail
and elderly inpatient wards, addressing the societal challenge of an aging
population. Frail and elderly patients typically suffer from multimorbidity and
require more care while receiving medical treatment. The aim of this research
is to assess how various predictive and prescriptive analytical methods, both
individually and in tandem, contribute to addressing the operational challenges
within an area of healthcare that is growing in demand. Clinical and
demographic patient attributes are gathered from more than 165,000 patient
records and used to explain and predict length of stay. To that extent, we
employ Classification and Regression Trees (CART) analysis to establish this
relationship. On the prescriptive side, deterministic and two-stage stochastic
programs are developed to determine how to optimally plan for beds and ward
staff with the objective to minimize cost. Furthermore, the two analytical
methodologies are linked by generating demand for the prescriptive models using
the CART groupings. The results show the linked methodologies provided
different but similar results compared to using averages and in doing so,
captured a more realistic real-world variation in the patient length of stay.
Our research reveals that healthcare managers should consider using predictive
and prescriptive models to make more informed decisions. By combining
predictive and prescriptive analytics, healthcare managers can move away from
relying on averages and incorporate the unique characteristics of their
patients to create more robust planning decisions, mitigating risks caused by
variations in demand.

通过结合预测性分析和指导性分析，本研究旨在评估不同的预测性和指导性分析方法对日益增长需求的医疗领域内运营挑战的贡献，并揭示在患者住院期间存在的真实世界的变化风险。

预测和预防分析多地点易碎和老年患者服务建模

Predictive and Prescriptive Analytics for Multi-Site Modeling of Frail  and Elderly Patient Services

This paper introduces a comprehensive, multi-stage machine learning
methodology that effectively integrates information systems and artificial
intelligence to enhance decision-making processes within the domain of
operations research. The proposed framework adeptly addresses common
limitations of existing solutions, such as the neglect of data-driven
estimation for vital production parameters, exclusive generation of point
forecasts without considering model uncertainty, and lacking explanations
regarding the sources of such uncertainty. Our approach employs Quantile
Regression Forests for generating interval predictions, alongside both local
and global variants of SHapley Additive Explanations for the examined
predictive process monitoring problem. The practical applicability of the
proposed methodology is substantiated through a real-world production planning
case study, emphasizing the potential of prescriptive analytics in refining
decision-making procedures. This paper accentuates the imperative of addressing
these challenges to fully harness the extensive and rich data resources
accessible for well-informed decision-making.

本文介绍了一个多阶段综合的机器学习方法，有效地将信息系统和人工智能相结合，以增强操作研究领域内的决策过程的效力。该方法能够解决现有解决方案的常见限制，例如忽略关键生产参数的数据驱动估计、仅生成点预测而不考虑模型不确定性以及缺乏关于不确定性来源的解释。我们的方法利用分位数回归森林生成间隔预测，同时采用了局部和全局版本的 SHapley 加性解释，用于研究预测过程监测问题。通过一个实际的生产计划案例研究，我们证明了所提出方法的实际适用性，强调了规范分析在完善决策程序方面的潜力。本文强调了解决这些挑战以充分利用广泛和丰富的数据资源来进行明智决策。

预测性流程监控中机器学习不确定性的量化和解释：运筹学视角

Quantifying and Explaining Machine Learning Uncertainty in Predictive  Process Monitoring: An Operations Research Perspective

A significant body of recent research in the field of Learning Analytics has
focused on leveraging machine learning approaches for predicting at-risk
students in order to initiate timely interventions and thereby elevate
retention and completion rates. The overarching feature of the majority of
these research studies has been on the science of prediction only. The
component of predictive analytics concerned with interpreting the internals of
the models and explaining their predictions for individual cases to
stakeholders has largely been neglected. Additionally, works that attempt to
employ data-driven prescriptive analytics to automatically generate
evidence-based remedial advice for at-risk learners are in their infancy.
eXplainable AI is a field that has recently emerged providing cutting-edge
tools which support transparent predictive analytics and techniques for
generating tailored advice for at-risk students. This study proposes a novel
framework that unifies both transparent machine learning as well as techniques
for enabling prescriptive analytics, while integrating the latest advances in
large language models. This work practically demonstrates the proposed
framework using predictive models for identifying at-risk learners of programme
non-completion. The study then further demonstrates how predictive modelling
can be augmented with prescriptive analytics on two case studies in order to
generate human-readable prescriptive feedback for those who are at risk using
ChatGPT.

该研究提出了一种新的框架，将透明的机器学习与数据驱动的处方分析技术相结合，同时结合大型语言模型的最新进展，实现了对非完成方案的有风险学习者进行预测模型的构建和处方分析的人类可读的反馈。