# Personalized and Reliable Decision Sets: Enhancing Interpretability in Clinical Decision Support Systems

arXiv: https://arxiv.org/abs/2107.07483 · HTML: https://mlsanity.com/paper/2107.07483/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 15 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [To Trust Or Not To Trust A Classifier](https://mlsanity.com/paper/1805.11783/) (2018)
- [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019)
- [The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations](https://mlsanity.com/paper/1907.09294/) (2019)
- [Interpretability of machine learning based prediction models in healthcare](https://mlsanity.com/paper/2002.08596/) (2020)
- [Falling Rule Lists](https://mlsanity.com/paper/1411.5899/) (2014)
- [Can You Trust This Prediction? Auditing Pointwise Reliability After Learning](https://mlsanity.com/paper/1901.00403/) (2019)
- [Interpretable Random Forests via Rule Extraction](https://mlsanity.com/paper/2004.14841/) (2020)
- [Improving the compromise between accuracy, interpretability and personalization of rule-based machine learning in medical problems](https://mlsanity.com/paper/2106.07827/) (2021)

## Built on by (most cited first, 2 of 2)

- [Modeling Clinical Concern Trajectories in Language Model Agents](https://mlsanity.com/paper/2604.27872/) (2026)
- [Evaluating LLM Uncertainty in Long-Form Generation Using Deterministic Ground Truth](https://mlsanity.com/paper/2607.03870/) (2026)

## Abstract (arXiv)

In this study, we present a novel clinical decision support system and discuss its interpretability-related properties. It combines a decision set of rules with a machine learning scheme to offer global and local interpretability. More specifically, machine learning is used to predict the likelihood of each of those rules to be correct for a particular patient, which may also contribute to better predictive performances. Moreover, the reliability analysis of individual predictions is also addressed, contributing to further personalized interpretability. The combination of these several elements may be crucial to obtain the clinical stakeholders' trust, leading to a better assessment of patients' conditions and improvement of the physicians' decision-making.