Risk prediction models are increasingly employed in clinical environments to support
population health management, quality assessment, and clinical decision support tools …

Abstract Model calibration, critical to the success and safety of clinical prediction models,
deteriorates over time in response to the dynamic nature of clinical environments. To support …

In evolving clinical environments, the accuracy of prediction models deteriorates over time.
Guidance on the design of model updating policies is limited, and there is limited exploration …

As the implementation of artificial intelligence (AI)-enabled tools is realized across diverse
clinical environments, there is a growing understanding of the need for ongoing monitoring …

Objective Clinical prediction models require updating as performance deteriorates over time.
We developed a testing procedure to select updating methods that minimizes overfitting …

Despite great promise, developing and implementing machine learning (ML) models for
healthcare remains a challenging engineering task. The progression of disease generates …

Background The assessment of calibration performance of risk prediction models based on
regression or more flexible machine learning algorithms receives little attention. Main text …

Predictive analytics in health care has generated increasing enthusiasm recently, as
reflected in a rapidly growing body of predictive models reported in literature and in real-time …

As an interdisciplinary technologically driven field, the science of informatics is rapidly
evolving. In this issue of Journal of the American Medical Informatics Association, we bring …

Background As the enthusiasm for integrating artificial intelligence (AI) into clinical care
grows, so has our understanding of the challenges associated with deploying impactful and …