TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods bmj.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from bmj.com Daily Mail and Mail on Sunday newspapers.
An external validation study evaluates the performance of a prediction model in new data, but many of these studies are too small to provide reliable answers. In the third article of their series on model evaluation, Riley and colleagues describe how to calculate the sample size required for external validation studies, and propose to avoid rules of thumb by tailoring calculations to the model and setting at hand.
External validation studies evaluate the performance of one or more prediction models (eg, developed previously using statistical, machine learning, or artificial intelligence approaches) in a different dataset to that used in the model development process.1 2 3 Part 2 in our series describes how to undertake a high quality external validation study,4 including the need to estimate model performance measures such as calibration (agreement between observed and predicted values), discrimination (separation between predicted values in those with and without an outcome event), o
The authors developed and validated an accurate, well-calibrated, easy-to-implement COVID-19 hospitalized patient deterioration index to identify patients at high or low risk of clinical deterioration.
AI is reshaping healthcare practice through innovative tools and technologies that augment the capabilities of healthcare providers while streamlining workflows and enhancing diagnostic accuracy