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Showing 1 to 5 of 5 for “"Clinical risk prediction"”.
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Clustering-Based Methods for Clinical Risk Prediction of Rare Missense Variants
A long-standing goal in clinical genomics is to map individual genetic variants to clinical outcomes. Typically, variants which lead to loss of function (e.g. nonsense or stop-codon inducing variants, frameshifts, or deletions) are more easily classified as pathogenic in an established disease …
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Application and Extension of Weighted Quantile Sum Regression for the Development of a Clinical Risk Prediction Tool
In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient’s health and the overall function of different organs. In this …
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Neurologic And Metabolic Safety Of Fluoroquinolones
… users to develop and validate two types of risk prediction models, LASSO and random forest, in predicting CNS and PNS dysfunction, which were outcomes found to be associated with fluoroquinolones in the first chapter. We assessed the accuracy and calibration of these models in a validation …
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Tailoring visual communication of cardiovascular disease risk to diverse populations: supporting informed decision-making in primary care
… socioeconomically disadvantaged populations. Risk prediction models embedded within patient decision aids aim to support informed decision-making in primary prevention. However, clinical tools use varied graphic formats with little consistency, and evidence on which visual formats best support …
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Evaluating and enhancing cardiovascular disease risk prediction with algorithmic fairness
… cause of morbidity and mortality worldwide, with risk prediction models in widespread clinical use. Yet much remains unknown about the performance of CVD risk prediction models in specific subgroups, and disparities in predictions can exacerbate health inequities. Algorithmic fairness, a research …