2022
DOI: 10.1186/s12882-022-02961-x
|View full text |Cite
|
Sign up to set email alerts
|

Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries

Abstract: Background We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas. Methods We used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonl… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0
1

Year Published

2023
2023
2024
2024

Publication Types

Select...
3
1

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(4 citation statements)
references
References 29 publications
0
3
0
1
Order By: Relevance
“…For an overview of the XGBoost logic, this non-linear machine learning model used the input (independent) variables in the training dataset to construct an array of decision trees in every possible combination to establish a series of thresholds that split variables to maximize the information gain ( 19 ). In our study, the XGBoost model predicted patient risk with excellent calibration and good validation.…”
Section: Discussionmentioning
confidence: 99%
“…For an overview of the XGBoost logic, this non-linear machine learning model used the input (independent) variables in the training dataset to construct an array of decision trees in every possible combination to establish a series of thresholds that split variables to maximize the information gain ( 19 ). In our study, the XGBoost model predicted patient risk with excellent calibration and good validation.…”
Section: Discussionmentioning
confidence: 99%
“…Another step is the analysis of risk factors, with an emphasis on the use of Gini feature importance as a parameter indicating critical factors for classifying patients and discrimination in risk models. Other authors mention its use in the assessment of unrelated and COVID-19-related mortality in dialysis patients [43,44]. It was also used in predicting the outcome in patients with lupus nephritis [42] and in estimating graft function in patients after kidney transplantation [45].…”
Section: Discussionmentioning
confidence: 99%
“…Un enfoque más novedoso para controlar el sesgo por selección fue presentado recientemente por Guinsburg et al 2 y se basó en el uso de una herramienta de machine-learning llamada DoWhy. DoWhy es una librería de Python de código abierto creada con suposiciones causales, organizada en torno a los cuatro pasos clave necesarios para cualquier análisis causal: modelar, identificar, estimar y refutar.…”
Section: Evidencia Científica Sobre Reducción De La Mortalidad En Arg...unclassified