Various examples are provided related to mHealth based risk stratification. In one example, a system includes a handheld echocardiography device that can generate ultrasound (US) images of a patient and processing circuitry comprising a processor and memory. The processing circuitry can receive the US...
3,4 The USPSTF reviewed evidence from randomized trials, observational studies, and diagnostic accuracy studies of risk stratification models in women without preexisting breast cancer or DCIS. Studies that included women with pathogenic BRCA1/2 genetic mutations were included in the review criteria; ...
random forest model developed using data from a single oncogenetic institution in Switzerland that focusses on counselling and testing for hereditary cancer syndromes [51]. Whilst the machine learning models were declared to outperform BOADICEA, no robust evaluation of model calibration was performed, a...
On the basis of these results, the overall C-Lung-RADS pipeline for the four-category risk stratification of nodules was developed, which integrated the classification tree in phase 1, the DCNN model in phase 2 and the gradient-boosting regression (GBR) model in phase 2+, executed sequentially...
We demonstrated that analyzing individuals' longitudinal data and exploiting the data combined from several decentralized sources can enhance risk stratification. Although the methods we used are well-developed, they have not been applied to NAFLD complication/fibrosis. A further literature search focusing...
Early detection is a principal strategy for improving GC patient outcomes; however, the need for a risk stratification approach to GC screening is particularly important for countries such as Singapore, where the absolute GC incidence is lower than in Japan. This is because early GC diagnosis is ...
External validation, modelupdating, and impact assessmentKarel G M Moons, 1 Andre Pascal Kengne, 1,2,3 Diederick E Grobbee, 1 Patrick Royston, 4Yvonne Vergouwe, 1 Douglas G Altman, 5 Mark Woodward 2,6ABSTRACTClinical prediction models are increasingly used tocomplement clinical reasoning and ...
we focus on two in this dissertation: survival risk prediction and optimal treatment decision.;In Chapter 2, we propose a new model-free machine learning method for risk classification and survival probability prediction, which plays an important role in patients' risk stratification, long-term diagn...
Risk stratification for reducing the burden of screening We will start by leaving aside the issue of overdiagnosis and consider what is arguably the more traditional approach, focusing only how a predictive marker could reduce the burden of screening. Although our interest is PRSs, we will use the...
between patients stratified into a low and a high risk group using the best performing models, which confirms their clinical potential. However, the results strongly depend on the process of selecting the stratification cut-off, and not necessarily on the performance of the risk model. This was ...