BACKGROUND: ML predictive models have shown their capability to improve risk prediction and assist medical decision-making, nevertheless, there is a lack of accuracy systems to early identify future rapid CKD progressors in Colombia and even in South...
BACKGROUND: Public awareness of artificial intelligence (AI) is increasing and this novel technology is being used for a range of everyday tasks and more specialist clinical applications. On a background of increasing waits for GP appointments alongs...
OBJECTIVES: We evaluated the applicability of a machine learning-based low-density lipoprotein-cholesterol (LDL-C) estimation method and the influence of the characteristics of the training datasets.
BACKGROUND: Explainability, the aspect of artificial intelligence-based decision support (ADS) systems that allows users to understand why predictions are made, offers many potential benefits. One common claim is that explainability increases user tr...
BACKGROUND: LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be ...
BACKGROUND: It is difficult for clinical laboratories to identify samples that are labelled with the details of an incorrect patient. Many laboratories screen for these errors with delta checks, with final decision-making based on manual review of re...