Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring. We perform a review and analysis of the multidisciplinary steps required to develop and rigorously evaluate predictive clinical decision support tools based on monitoring. Development and evaluation of waveform-based variability-derived predictive models involves a multistep, multidisciplinary approach. The stepwise processes involves data science (data collection, waveform processing, variability analysis, statistical analysis, machine learning, predictive modelling), CDSS development (iterative research prototype evolution to commercial tool), and clinical research (observational and interventional implementation studies, followed by feasibility then definitive randomized controlled trials), and poses unique challenges (including technical, analytical, psychological, regulatory and commercial). The proposed roadmap provides guidance for the development and evaluation of novel predictive CDSS tools with potential to help transform monitoring and improve care.

中文翻译：

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监测发展路线图：开发和评估基于波形的可变性衍生人工智能驱动的预测性临床决策支持软件工具


连续波形监测是有风险或危重症患者的标准护理。从波形得出的心率、呼吸频率和血压变异性包含有用的诊断和预后信息;当与机器学习相结合时，可以提供与疾病严重程度和/或生理储备减少相关的预测指标。将预测模型集成到临床决策支持软件 （CDSS） 工具中代表了监查的潜在演变。我们对开发和严格评估基于监测的预测性临床决策支持工具所需的多学科步骤进行审查和分析。基于波形的可变性衍生预测模型的开发和评估涉及多步骤、多学科的方法。逐步过程涉及数据科学（数据收集、波形处理、可变性分析、统计分析、机器学习、预测建模）、CDSS 开发（迭代研究原型演变为商业工具）和临床研究（观察和干预实施研究，然后是可行性，然后是确定的随机对照试验），并带来独特的挑战（包括技术、分析、心理、监管和商业）。拟议的路线图为开发和评估新型预测性 CDSS 工具提供了指导，这些工具可能有助于转变监测和改善护理。