Semantic Scholar Open Access 2018 661 sitasi

Interpretable Machine Learning in Healthcare

M. Ahmad A. Teredesai C. Eckert

Abstrak

This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.

Topik & Kata Kunci

Computer Science

Penulis (3)

M. Ahmad

A. Teredesai

C. Eckert

Format Sitasi

APA MLA BibTeX

Ahmad, M., Teredesai, A., Eckert, C. (2018). Interpretable Machine Learning in Healthcare. https://doi.org/10.1145/3233547.3233667

Akses Cepat

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Lihat di Sumber doi.org/10.1145/3233547.3233667

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 661×
Sumber Database: Semantic Scholar
DOI: 10.1145/3233547.3233667
Akses: Open Access ✓