Summary of Selecting Interpretability Techniques For Healthcare Machine Learning Models, by Daniel Sierra-botero et al.
Selecting Interpretability Techniques for Healthcare Machine Learning models
by Daniel Sierra-Botero, Ana Molina-Taborda, Mario S. Valdés-Tresanco, Alejandro Hernández-Arango, Leonardo Espinosa-Leal, Alexander Karpenko, Olga Lopez-Acevedo
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the development of interpretable machine learning algorithms to aid healthcare professionals in decision-making scenarios. The Predictive, Descriptive, and Relevant (PDR) framework defines interpretable machine learning as a model that explicitly outlines relationships relevant to its functioning. The study presents an overview of eight algorithms, both post-hoc and model-based, designed for interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In healthcare, doctors need tools to help them make good decisions. This paper talks about special computer programs that can do just that. It explains what makes these programs “interpretable,” which means they can show how they came up with their answers. The paper looks at eight different types of these algorithms and how they work. |
Keywords
* Artificial intelligence * Machine learning
