基于预测模型和蒙特卡罗树搜索的服务选择
Service Selection using Predictive Models and Monte-Carlo Tree Search
摘要：
本文提出了一种自动化服务选择的方法，以提高治疗效果，降低再住院费用。使用国家家庭和临终关怀调查（NHHCS）数据集开发了一个预测模型，以量化护理服务对再住院风险的影响。通过考虑患者的特征和其他选定的服务，该模型能够指示针对特定NHHCS患者的服务组合的总体有效性。所开发的模型被纳入蒙特卡罗树搜索（MCTS），以确定最佳的服务组合，最大限度地减少紧急再住院的风险。在这种情况下，MCTS用作风险最小化算法，在搜索期间使用预测模型进行指导。在NHHCS数据集上使用这种方法，与临床医生最初的选择相比，观察到再次住院的风险显著降低。风险平均降低11.89个百分点。在最高风险类别的NHHCS患者中观察到平均约40个百分点的更高降低。这些结果似乎表明，在不久的将来，改善服务选择有巨大的潜力。
英文摘要：
This article proposes a method for automated service selection to improve treatment efficacy and reduce re-hospitalization costs. A predictive model is developed using the National Home and Hospice Care Survey (NHHCS) dataset to quantify the effect of care services on the risk of re-hospitalization. By taking the patient's characteristics and other selected services into account, the model is able to indicate the overall effectiveness of a combination of services for a specific NHHCS patient. The developed model is incorporated in Monte-Carlo Tree Search (MCTS) to determine optimal combinations of services that minimize the risk of emergency re-hospitalization. MCTS serves as a risk minimization algorithm in this case, using the predictive model for guidance during the search. Using this method on the NHHCS dataset, a significant reduction in risk of re-hospitalization is observed compared to the original selections made by clinicians. An 11.89 percentage points risk reduction is achieved on average. Higher reductions of roughly 40 percentage points on average are observed for NHHCS patients in the highest risk categories. These results seem to indicate that there is enormous potential for improving service selection in the near future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
