Personalized and Reliable Decision Sets: Enhancing Interpretability in Clinical Decision Support Systems
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 15 Jul 2021.
Builds on
8 of 21 references.
References · 8
- 2018To Trust Or Not To Trust A Classifiercited by 205
- 2019What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Usecited by 134
- 2019The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanationscited by 97
- 2020Interpretability of machine learning based prediction models in healthcarecited by 75
- 2014Falling Rule Listscited by 58
- 2019Can You Trust This Prediction? Auditing Pointwise Reliability After Learningcited by 28
- 2020Interpretable Random Forests via Rule Extractioncited by 23
- 2021Improving the compromise between accuracy, interpretability and personalization of rule-based machine learning in medical problemscited by 3
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Citing papers per year: 2026: 1.
1 paper.