arXiv subjects

Keren Cohen

Publications and source records attributed to Keren Cohen.

3 recordsLinked to original sources

arXiv2026

Kidney function and kidney failure prediction in a large multiethnic population

Background: Patients with chronic kidney disease (CKD) experience worsening kidney function and develop subsequent kidney failure at different rates. Accurate estimates of a patient's current and future kidney function are needed for optimal clinical decision-making. Methods: To compare current and previously recommended equations for estimating kidney function and risk of kidney failure across a large multiethnic population, we conducted a retrospective multicenter cohort study of primary care, acute care, and hospital settings. Our study population comprised 1,909,042 adults with at least one recorded serum creatinine measurement during 2012-2014, with follow-up until January 2025. Our primary outcomes were the area under the receiver operating characteristic curve and prevalence of CKD by stage across regions of origin. Findings: GFR estimates from the two race-stratified equations (2006 MDRD and 2009 CKD-EPI) were similarly calibrated. GFR estimates from the two race-neutral equations diverged, with 2021 EKFC producing lower GFR estimates and 2021 CKD-EPI producing higher GFR estimates compared to prior equations. The oldest equation, 2006 MDRD, was the most discriminative of kidney failure within 5 years while the newer European equation, 2021 EKFC, was the least discriminative, with AUROCs of 0.862 (95% CI, 0.855-0.869) and 0.846 (95% CI, 0.838-0.853), respectively. The age-adjusted prevalence of CKD varied by the choice of eGFR equation (ranging from 8.5% for 2021 CKD-EPI to 10.8% for EKFC) and across regions of birth (ranging from 8.9% for East Sub-Saharan Africa to 15.3% for South Asia). Interpretation: Using 10 years of follow-up for nearly 2 million individuals, our study demonstrates how newly developed US and European equations diverge from previously recommended equations with broad clinical and epidemiological implications.

q-bio.QM↗

arXiv2019

Topological based classification of paper domains using graph convolutional networks

The main approaches for node classification in graphs are information propagation and the association of the class of the node with external information. State of the art methods merge these approaches through Graph Convolutional Networks. We here use the association of topological features of the nodes with their class to predict this class. Moreover, combining topological information with information propagation improves classification accuracy on the standard CiteSeer and Cora paper classification task. Topological features and information propagation produce results almost as good as text-based classification, without no textual or content information. We propose to represent the topology and information propagation through a GCN with the neighboring training node classification as an input and the current node classification as output. Such a formalism outperforms state of the art methods.

cs.SI↗

arXiv2018

A combined network and machine learning approaches for product market forecasting

Sustainable financial markets play an important role in the functioning of human society. Still, the detection and prediction of risk in financial markets remain challenging and draw much attention from the scientific community. Here we develop a new approach based on combined network theory and machine learning to study the structure and operations of financial product markets. Our network links are based on the similarity of firms' products and are constructed using the Securities Exchange Commission (SEC) filings of US listed firms. We find that several features in our network can serve as good precursors of financial market risks. We then combine the network topology and machine learning methods to predict both successful and failed firms. We find that the forecasts made using our method are much better than other well-known regression techniques. The framework presented here not only facilitates the prediction of financial markets but also provides insight and demonstrate the power of combining network theory and machine learning.

physics.soc-ph↗