Advances in medical machine learning are expected to help personalize care, improve outcomes, and reduce wasteful spending. In quantifying potential benefits, it is important to account for constraints arising from clinical workflows. Practice variation is known to influence the accuracy and generalizability of predictive models, but its effects on cost-effectiveness and utilization are less well-described. A simulation-based approach by Mišić and colleagues goes beyond simple performance metrics to evaluate how process variables may influence the impact and financial feasibility of clinical prediction algorithms.

中文翻译：

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超越性能指标：临床机器学习的建模结果和成本

医疗机器学习的进步有望帮助个性化护理、改善结果并减少浪费。在量化潜在收益时，重要的是要考虑临床工作流程产生的限制。众所周知，实践变化会影响预测模型的准确性和普遍性，但其对成本效益和利用率的影响却没有得到很好的描述。Mišić 及其同事基于模拟的方法超越了简单的性能指标来评估过程变量如何影响临床预测算法的影响和财务可行性。