Literature DB >> 30617317

Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data.

Stefan Ravizza1, Tony Huschto2, Anja Adamov1, Lars Böhm1, Alexander Büsser1, Frederik F Flöther1, Rolf Hinzmann2, Helena König2, Scott M McAhren3, Daniel H Robertson4, Titus Schleyer5, Bernd Schneidinger2, Wolfgang Petrich6.   

Abstract

Diagnostic procedures, therapeutic recommendations, and medical risk stratifications are based on dedicated, strictly controlled clinical trials. However, a plethora of real-world medical data exists, whereupon the increase in data volume comes at the expense of completeness, uniformity, and control. Here, a case-by-case comparison shows that the predictive power of our real world data-based model for diabetes-related chronic kidney disease outperforms published algorithms, which were derived from clinical study data.

Entities:  

Mesh：

Year:  2019        PMID： 30617317     DOI： 10.1038/s41591-018-0239-8

Source DB:  PubMed          Journal:  Nat Med        ISSN： 1078-8956            Impact factor:   53.440


  34 in total

1.  12th Roche Diabetes Care Network Meeting: April 11-13, 2019, Copenhagen, Denmark.

Authors:  Christopher G Parkin; Christine Zepezauer; Rolf Hinzmann
Journal:  Diabetes Technol Ther       Date:  2020-01-14       Impact factor: 6.118

2.  On the explainability of hospitalization prediction on a large COVID-19 patient dataset.

Authors:  Ivan Girardi; Panagiotis Vagenas; Dario Arcos-D Iaz; Lydia Bessa I; Alexander Bu Sser; Ludovico Furlan; Raffaello Furlan; Mauro Gatti; Andrea Giovannini; Ellen Hoeven; Chiara Marchiori
Journal:  AMIA Annu Symp Proc       Date:  2022-02-21

3.  The efficacy of canagliflozin in diabetes subgroups stratified by data-driven clustering or a supervised machine learning method: a post hoc analysis of canagliflozin clinical trial data.

Authors:  Xiantong Zou; Qi Huang; Yingying Luo; Qian Ren; Xueyao Han; Xianghai Zhou; Linong Ji
Journal:  Diabetologia       Date:  2022-07-08       Impact factor: 10.460

4.  Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database.

Authors:  Veronica Brady; Meagan Whisenant; Xueying Wang; Vi K Ly; Gen Zhu; David Aguilar; Hulin Wu
Journal:  Diabetes Spectr       Date:  2022-01-11

5.  Artificial Intelligence in the Identification, Management, and Follow-Up of CKD.

Authors:  Navdeep Tangri; Thomas W Ferguson
Journal:  Kidney360       Date:  2022-01-14

Review 6.  Big Data in Nephrology.

Authors:  Navchetan Kaur; Sanchita Bhattacharya; Atul J Butte
Journal:  Nat Rev Nephrol       Date:  2021-06-30       Impact factor: 28.314

7.  Deep learning approach for diabetes prediction using PIMA Indian dataset.

Authors:  Huma Naz; Sachin Ahuja
Journal:  J Diabetes Metab Disord       Date:  2020-04-14

8.  Active, continual fine tuning of convolutional neural networks for reducing annotation efforts.

Authors:  Zongwei Zhou; Jae Y Shin; Suryakanth R Gurudu; Michael B Gotway; Jianming Liang
Journal:  Med Image Anal       Date:  2021-03-24       Impact factor: 13.828

9.  Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset.

Authors:  Mike D Rinderknecht; Yannick Klopfenstein
Journal:  NPJ Digit Med       Date:  2021-07-20

Review 10.  Applications of machine learning methods in kidney disease: hope or hype?

Authors:  Lili Chan; Akhil Vaid; Girish N Nadkarni
Journal:  Curr Opin Nephrol Hypertens       Date:  2020-05       Impact factor: 3.416
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.