Who Cited It

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

2018 · JAMA Internal Medicine · 1,409 citations · 1 from inside this corpus

Milena Gianfrancesco, Suzanne Tamang, Jinoos Yazdany, Gabriela Schmajuk

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data (2018)Potential Biases in Machine L…Scalable and accurate deep learning with electronic health records (2018)Scalable and accurate deep le…Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Excit… (2016)Guest Editorial Deep Learning…Machine Learning in Medicine (2019)Machine Learning in Medicine
3 of 3 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
Scalable and accurate deep learning with electronic health records20182,398
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Excit…20161,748

What cites it, inside the corpus

PaperYearCited
Machine Learning in Medicine20194,382

Links

DOI · Open access copy · OpenAlex record

Topics

Machine Learning in HealthcareComputer Science
Artificial Intelligence in Healthcare and EducationMedicine
Artificial Intelligence in HealthcareHealth Professions

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports4 author record(s) attached.
  • supports26 reference(s) recorded.
  • supportsThe DOI's year agrees with the publication year.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:59+00:00.

sha256 59007f057c7dae86…