Machine learning techniques are effective for building predictive models because they are good at identifying patterns in large datasets. Development of a model for complex real life problems often stops at the point of publication, proof of concept or when made accessible through some mode of deployment. However, a model in the medical domain risks becoming obsolete as soon as patient demographic changes. The maintenance and monitoring of predictive models post-publication is crucial to guarantee their safe and effective long term use. As machine learning techniques are effectively trained to look for patterns in available datasets, the performance of a model for complex real life problems will not peak and remain fixed at the point of publication or even point of deployment. Rather, data changes over time, and they also changed when models are transported to new places to be used by new demography.

翻译：机器学习技术因其擅长识别大型数据集中的模式，而成为构建预测模型的有效手段。针对复杂现实问题开发模型的过程，通常止步于发表、概念验证或通过某种部署方式实现可用性。然而，医疗领域的模型一旦患者人口统计特征发生变化，就面临过时风险。预测模型发表后的维护与监控对于确保其长期安全有效使用至关重要。由于机器学习技术本质上是针对现有数据集中的模式进行训练，针对复杂现实问题的模型性能不会在发表时甚至部署时达到峰值并保持恒定。相反，数据会随时间推移而变化，当模型被迁移至新地区由新人群使用时，数据同样会发生改变。