Interpretable Machine Learning in Healthcare
Abstract
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The human-machine combination is a powerful tool to facilitate patients and develop high-quality treatments.
“This human-machine combination is a powerful tool to facilitate the patients and develop high-quality treatments [21].”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [21]
The citation lists haven’t been fetched yet
References 15 · Cited by 583
- Authors
- Ahmad, M. A., Eckert, C., & Teredesai, A.
- Affiliations
- University of Washington Tacoma (US)
- University of Washington (US)
- Venue
- Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
- Publisher
- ACM
- Published
- 2018-08-15
- Pages
- 559-560
- OpenAlex
- W2888487581
- Cited by
- 583
- References
- 15
- Citations by year
- 2026: 37 · 2025: 77 · 2024: 88 · 2023: 93 · 2022: 112 · 2021: 102 · 2020: 55 · 2019: 19
- FWCI
- 17.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- model interpretability, explainable machine learning, health care, algorithm selection, clinical decision support
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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