# Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/potential-biases-in-machine-learning-algorithms-using-electronic-health-record/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Milena Gianfrancesco,Suzanne Tamang,Jinoos Yazdany,Gabriela Schmajuk |
| Citations | 1,368 |
| DOI | 10.1001/jamainternmed.2018.3763 |
| Fields | Computer Science,Health Professions,Medicine |
| Open Access | true |
| OA Status | green |
| OA URL | https://www.ncbi.nlm.nih.gov/pmc/articles/6347576 |
| OpenAlex ID | https://openalex.org/W2888109941 |
| PMID | 30128552 |
| Type | article |
| Year | 2018 |

## Paper authors

- [Jinoos Yazdany](https://scholariq.org/researchers/jinoos-yazdany/)

## Paper journal

- [JAMA Internal Medicine](https://scholariq.org/journals/jama-internal-medicine/)

## Paper primary topic

- [Machine Learning in Healthcare](https://scholariq.org/topics/machine-learning-in-healthcare/)

## Paper topics

- [Machine Learning in Healthcare](https://scholariq.org/topics/machine-learning-in-healthcare/)
- [Artificial Intelligence in Healthcare and Education](https://scholariq.org/topics/artificial-intelligence-in-healthcare-and-education/)
- [Artificial Intelligence in Healthcare](https://scholariq.org/topics/artificial-intelligence-in-healthcare/)

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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.