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Evaluation of clinical prediction models (part 2): how to undertake an external validation study

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 ....

United Kingdom , Lucinda Archer , Lauraj Bonnett , Glenp Martin , Paula Dhiman , Richardd Riley , Kym Ie Snell , Garys Collins , University Of Birmingham , Duke Clinical Research Institute , Cancer Research United Kingdom , Better Methods Research , Health Improvement Network , Research Council , Sciences Research Council , United Kingdom Department Of Health , Birmingham Biomedical Research Centre , University Hospitals Birmingham , National Institute For Health , Clinical Practice Research Datalink , Care Research , Transparent Reporting , Individual Prognosis Or Diagnosis , United Kingdom Biobank , Risk Of Bias , Participant Selection ,

"A Comprehensive Overview of IoT-Based Federated Learning: Focusing on " by Naghmeh Khajehali, Jun Yan et al.

The integration of the Internet of Things (IoT) with machine learning (ML) is revolutionizing how services and applications impact our daily lives. In traditional ML methods, data are collected and processed centrally. However, modern IoT networks face challenges in implementing this approach due to their vast amount of data and privacy concerns. To overcome these issues, federated learning (FL) has emerged as a solution. FL allows ML methods to achieve collaborative training by transferring model parameters instead of client data. One of the significant challenges of federated learning is that IoT devices as clients usually have different computation and communication capacities in a dynamic environment. At the same time, their network availability is unstable, and their data quality varies. To achieve high-quality federated learning and handle these challenges, designing the proper client selection process and methods are essential, which involves selecting suitable clients from the ....

Client Selection , Device Selection , Federated Learning , Machine Learning , Mode Selection , Participant Selection ,