预后模型将成为自身成功的受害者，除非……
Prognostic models will be victims of their own success, unless….
机构信息
Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
出版信息
J Am Med Inform Assoc. 2019 Dec 1;26(12):1645-1650. doi: 10.1093/jamia/ocz145.
Predictive analytics have begun to change the workflows of healthcare by giving insight into our future health. Deploying prognostic models into clinical workflows should change behavior and motivate interventions that affect outcomes. As users respond to model predictions, downstream characteristics of the data, including the distribution of the outcome, may change. The ever-changing nature of healthcare necessitates maintenance of prognostic models to ensure their longevity. The more effective a model and intervention(s) are at improving outcomes, the faster a model will appear to degrade. Improving outcomes can disrupt the association between the model's predictors and the outcome. Model refitting may not always be the most effective response to these challenges. These problems will need to be mitigated by systematically incorporating interventions into prognostic models and by maintaining robust performance surveillance of models in clinical use. Holistically modeling the outcome and intervention(s) can lead to resilience to future compromises in performance.
预测分析开始通过洞察我们未来的健康状况来改变医疗保健的工作流程。将预测模型部署到临床工作流程中应该会改变行为，并促使采取影响结果的干预措施。随着用户对模型预测的响应，数据的下游特征（包括结果的分布）可能会发生变化。医疗保健的不断变化性质需要维护预测模型以确保其长期存在。模型和干预措施在改善结果方面越有效，模型看起来降级的速度就越快。改善结果可能会破坏模型预测因子与结果之间的关联。模型重新拟合并不总是应对这些挑战的最有效方法。通过系统地将干预措施纳入预测模型并对临床使用中的模型进行稳健的性能监测，可以缓解这些问题。全面建模结果和干预措施可以提高对未来性能下降的适应能力。
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