# A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-new-machine-learning-approach-for-predicting-the-response-to-anemia-treatment/

## Facts

| Field | Value |
| --- | --- |
| Author Names | C. Barbieri,Flavio Mari,Andrea Stopper,Emanuele Gatti,Pablo Escandell-Montero,José M. Martínez-Martínez,José D. Martín‐Guerrero |
| Citations | 86 |
| DOI | 10.1016/j.compbiomed.2015.03.019 |
| Fields | Medicine |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2015985912 |
| PMID | 25864164 |
| Type | article |
| Year | 2015 |

## Paper authors

- [Emanuele Gatti](https://scholariq.org/researchers/emanuele-gatti/)

## Paper primary topic

- [Erythropoietin and Anemia Treatment](https://scholariq.org/topics/erythropoietin-and-anemia-treatment/)

## Paper topics

- [Erythropoietin and Anemia Treatment](https://scholariq.org/topics/erythropoietin-and-anemia-treatment/)
- [Iron Metabolism and Disorders](https://scholariq.org/topics/iron-metabolism-and-disorders/)
- [Hemoglobinopathies and Related Disorders](https://scholariq.org/topics/hemoglobinopathies-and-related-disorders/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.