Pruski Michal, Willis Simone, Withers Kathleen
School of Health Sciences, The University of Manchester, Manchester, UK.
CEDAR, Cardiff and Vale UHB, Cardiff, UK.
BMC Med Inform Decis Mak. 2025 Jul 4;25(1):250. doi: 10.1186/s12911-025-03083-8.
This review summarises the studies which combined Patient Reported Outcome Measures (PROMs) and Machine Learning statistical computational techniques, to predict patient post-intervention outcomes. The aim of the project was to inform those working in value-based healthcare how Machine Learning can be used with PROMs to inform clinical practice.
A systematic search strategy was developed and run in six databases. The records were reviewed by a reviewer if they matched the review scope, and these decisions were scrutinised by a second reviewer.
82 records pertaining to 73 studies were identified. The review highlights the breadth of PROMs tools investigated, and the wide variety of Machine Learning techniques utilised across the studies. The findings suggest that there has been some success in predicting post-intervention patient outcomes. Nevertheless, there is no clear best performing Machine Learning approach to analyse this data, and while baseline PROMs scores are often a key predictor of post-intervention scores, this cannot always be assumed to be the case. Moreover, even when studies looked at similar conditions and patient groups, often different Machine Learning techniques performed best in each study.
This review highlights that there is a potential for PROMs and Machine Learning methodology to predict patient post-intervention outcomes, but that best performing models from other previous studies cannot simply be adopted in new clinical contexts.
本综述总结了将患者报告结局测量（PROMs）与机器学习统计计算技术相结合以预测患者干预后结局的研究。该项目的目的是告知基于价值的医疗保健领域的工作人员机器学习如何与PROMs一起用于指导临床实践。
制定了一项系统检索策略并在六个数据库中运行。如果记录符合综述范围，则由一名评审员进行审查，这些决定由另一名评审员进行仔细检查。
识别出与73项研究相关的82条记录。该综述突出了所研究的PROMs工具的广度，以及各项研究中使用的机器学习技术的多样性。研究结果表明，在预测干预后患者结局方面取得了一些成功。然而，对于分析这些数据，没有明确表现最佳的机器学习方法，虽然基线PROMs分数通常是干预后分数的关键预测指标，但不能总是假定如此。此外，即使研究关注相似的病情和患者群体，通常在每项研究中表现最佳的机器学习技术也有所不同。
本综述强调，PROMs和机器学习方法有潜力预测患者干预后结局，但不能简单地在新的临床环境中采用以往其他研究中表现最佳的模型。
