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：机器学习技术因擅长识别大型数据集中的模式，故能有效构建预测模型。对于复杂的现实问题，模型开发往往止步于论文发表、概念验证或通过某种部署方式实现可访问性。然而，在医疗领域，一旦患者人口统计学特征发生变化，模型便可能面临过时风险。预测模型在发表后的维护与监测，对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，其处理复杂现实问题的性能不会在发表甚至部署时达到峰值并保持不变。相反，数据会随时间推移而变化，当模型被迁移至新环境、服务于新人群时，数据同样会发生改变。