Rebecca Giddings MFPH , Anabel Joseph BSc , Thomas Callender MFPH , Prof Sam M Janes PhD , Prof Mihaela van der Schaar PhD , Jessica Sheringham FFPH , Neal Navani PhD
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引用次数: 0
Abstract
Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.
基于机器学习(ML)的风险预测模型有可能在多个方面为医疗机构提供支持;然而,此类模型的使用却很少。我们旨在回顾已发表文献中医疗保健专业人员(HCP)和患者对 ML 风险预测模型的看法,为未来风险预测模型的开发提供参考。经过数据库和引文检索,我们确定了 41 篇适合纳入的文章。文章质量参差不齐,其中定性研究的效果最好。总体而言,人们对 ML 风险预测模型的看法是积极的。医疗保健人员和患者认为模型有可能为医疗保健环境带来更多益处。但仍有一些保留意见,例如,对模型开发数据质量的担忧,以及对使用 ML 模型后意外后果的恐惧。我们发现,公众对这些模型的看法可能比医护人员更消极,而且所担心的问题(如对工作量的额外要求)并不总是在实践中得到证实。由于患者和公众研究的数量较少、缺乏参与者的种族多样性以及文章质量的差异,我们得出的结论并不全面。我们发现了知识方面的差距(尤其是代表性不足群体的观点)以及模型解释和警报的最佳方法,这些都需要未来的研究。
期刊介绍:
The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health.
The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health.
We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.