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Clinical Collabsheets 53 Questions to Guide a Clinical Collaboration

机译:临床上的Collabeet 53个问题,以指导临床协作

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Clinical Machine Learning (ML) is a rapidly-growing field due to the digitization of hospital records, recent advances in ML techniques, and the ability to leverage increasing computational power for large and complex models. The high stakes and often unintuitive nature of clinical data make effective collaboration between clinicians and ML researchers one of the most important aspects of working in this interdisciplinary space. However, there are few resources codifying best practices for collaboration on Clinical ML projects. In this paper, we interviewed 18 experts in the Clinical ML field and distilled their advice and experiences into a list of questions (a Clinical Collabsheet) ML scientists and clinicians can use to promote effective discussion when working on a new project. We intend this for a broad audience as checklist of discussion points to hit at a kickoff meeting. This resource will enable more successful partnerships in Clinical ML with improved interdisciplinary communication and organization.
机译:临床机学习(ML)是一种快速增长的领域,由于医院记录的数字化,ML技术的最新进展,以及利用增加的大型和复杂模型的计算能力。临床数据的高赌注和往往的性质,临床医生和ML研究人员之间的有效合作在这个跨学科空间中工作的最重要方面之一。但是,在临床ML项目上,很少有资源编纂用于合作的最佳实践。在本文中,我们在临床ML领域采访了18个专家,并将其建议和经验蒸发到一个问题清单(临床同源)ML科学家和临床医生可以用来在新项目工作时促进有效讨论。我们打算为广泛的受众视为讨论点的清单,以便在开球会议上击中。该资源将在临床ML中实现更成功的伙伴关系,具有改进的跨学科沟通和组织。

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