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:机器学习技术对于建立预测模型是有效的,因为它们能很好地查明大型数据集中的模式。为复杂的实际生活问题开发模型往往在公布、证明概念或通过某种部署模式获得时停止。然而,医疗领域的模式一旦病人人口变化,就有可能过时。维护和监测预测模型后出版对于保证其安全有效的长期使用至关重要。随着机器学习技术得到有效培训,以寻找现有数据集中的模式,复杂实际生活问题模型的性能不会达到高峰,在公布或甚至部署点保持不变。相反,数据随着时间变化,当模型被迁移到新的人口结构使用的新地点时,数据也会发生变化。</s>