Semantic Scholar Open Access 2021 208 sitasi

Mitigating bias in machine learning for medicine

K. N. Vokinger S. Feuerriegel A. Kesselheim

Lihat Sumber DOI

Abstrak

Several sources of bias can affect the performance of machine learning systems used in medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias across the different development steps of machine learning-based systems for medical applications. Vokinger et al. discuss potential sources of bias in machine learning systems used in medicine. The authors propose solutions to mitigate bias across the different stages of model development, from data collection and preparation to model evaluation and application.

Topik & Kata Kunci

Medicine

Penulis (3)

K. N. Vokinger

S. Feuerriegel

A. Kesselheim

Format Sitasi

APA MLA BibTeX

Vokinger, K.N., Feuerriegel, S., Kesselheim, A. (2021). Mitigating bias in machine learning for medicine. https://doi.org/10.1038/s43856-021-00028-w

Akses Cepat

Lihat di Sumber doi.org/10.1038/s43856-021-00028-w

Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Total Sitasi: 208×
Sumber Database: Semantic Scholar
DOI: 10.1038/s43856-021-00028-w
Akses: Open Access ✓