Predictive Analytics in Hemodialysis: Enabling Precision Care for Patient with ESKD

基于机器学习(ML)的临床预测模型(CPM)在过去得到了迅速发展 几年，成为医疗保健的核心组成部分。这些工具在以下方面显示出巨大的前景 将即将到来的健康结果告知提供者和患者，最终允许 更加个性化的患者护理。在我们的父代R01中，我们正在开发基于ML的死亡率 血液透析患者的预测模型。此工具的目标是预测两者的短期 和长期死亡风险，以促进患者和 供应商。重要的是，基于ML的CPM存在许多固有的伦理问题。这些 包括对道德表现(即算法偏差)、道德使用(即确保 产出得到适当的解释)和合乎道德的实施(即工具得到适当的整合 进入临床工作流程。对于目前的nosi，我们建议探讨道德使用的问题。 基于ML的黑石物理服务器。为了做到这一点，我们将对患者及其护理人员进行重点小组调查， 供应商和数据科学家。我们将解决基于ML的黑石服务器的信任问题， 了解风险和最佳风险沟通、与CPM和AI/ML工具的交互以及 使用CPM赋予患者权力，促进共享决策。我们会比较一下 各选区的回应。我们将利用我们的发现来开发以实践为导向的指导 它既面向基于ML的服务器的开发人员，也面向用户。

Machine learning (ML) based clinical prediction models (CPMs) have proliferated over the past few years, becoming a central component of healthcare. These tools show great promise in informing both providers and patients of impending health outcomes, ultimately allowing for greater personalization of patient care. In our parent R01 we are developing a ML based mortality prediction model for patients undergoing hemodialysis. The goal of this tool is to predict both short and long term mortality risk in order to promote shared decision making between patients and providers. Importantly, there are a number of ethical concerns inherent in ML based CPMs. These include consideration of ethical performance (i.e., algorithmic bias), ethical usage (i.e., ensuring outputs are properly interpreted) and ethical implementation (i.e., the tool is properly integrated into the clinical workflow. For this current NOSI we propose to explore questions of ethical usage of ML based CPMs. To do this, we will conduct focus group of patients & their caregivers, providers and data scientists. We will address questions of trust in ML based CPMs, understanding of risk and optimal risk communication, interaction with CPMs and AI/ML tools and usage of CPMs to empower patients and promote shared decision-making. We will compare responses across constituencies. We will use our findings to develop practice oriented guidances that target both develops and users of ML based CPMs.