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.
Review highlights the potential of AI models in predicting cardiovascular disease risks but emphasizes the need for independent external validation to ensure their clinical applicability.
External validation studies are an important but often neglected part of prediction model research. In this article, the second in a series on model evaluation, Riley and colleagues explain what an external validation study entails and describe the key steps involved, from establishing a high quality dataset to evaluating a model’s predictive performance and clinical usefulness.
A clinical prediction model is used to calculate predictions for an individual conditional on their characteristics. Such predictions might be of a continuous value (eg, blood pressure, fat mass) or the probability of a particular event occurring (eg, disease recurrence), and are often in the context of a particular time point (eg, probability of disease recurrence within the next 12 months). Clinical prediction models are traditionally based on a regression equation but are increasingly derived using artificial intelligence or machine learning methods (eg, random forests, neural networks). Regardless of the
Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation, as well as exploring heterogeneity, fairness, and generalisability in model performance.
Healthcare decisions for individuals are routinely made on the basis of risk or probability.1 Whether this probability is that a specific outcome or disease is present (diagnostic) or that a specific outcome will occur in the future (prognostic), it is important to know how these probabilities are estimated and whether they are accurate. Clinical prediction models estimate outcome risk for an individual conditional on their characteristics of multiple predictors (eg, age, family history, symptoms, blood pressure). Examples include the ISARIC (International Severe
James Neal Russell and colleagues report a global neonatal sepsis observational cohort study (NeoOBS) exploring patterns of antibiotic use, pathogens and prediction of mortality in hospitalised neonates and young infants with sepsis.