论文标题
使用预测模型和蒙特卡洛树搜索的服务选择
Service Selection using Predictive Models and Monte-Carlo Tree Search
论文作者
论文摘要
本文提出了一种自动化服务选择的方法，以提高治疗效果并降低住院成本。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，以量化护理服务对重新住院风险的影响。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患者的服务组合的整体有效性。开发的模型纳入了蒙特卡洛树搜索（MCT），以确定最佳服务的最佳组合，以最大程度地减少紧急重新住院的风险。在这种情况下，MCT使用搜索过程中的指导模型是一种风险最小化算法。与临床医生的原始选择相比，使用NHHCS数据集上的这种方法可显着降低重新住院的风险。平均可以实现11.89个百分点的风险。对于最高风险类别的NHHCS患者，平均观察到大约40个百分点的降低。这些结果似乎表明，在不久的将来，改善服务选择的潜力很大。
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.
