ArticleBMC medical informatics and decision making2025
Assisting clinical diagnosis with interpretable fuzzy probabilistic modelling.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Socioeconomic determinants of malaria in Ugandan children: An interpretable machine learning approach for public health policy.PLOS digital health · 2026Article
- Leveraging human-centered AI for clinical decision-making: a transparent, accurate rule extractor using non-dominated sorting genetic algorithm.BMC medical informatics and decision making · 2026Article
- The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review.Antibiotics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe need for transparency and interpretability is a fundamental theme to be addressed by Artificial Intelligence (AI) research, especially in high-risk applications such as healthcare. In this work, we propose Fuzzy Sets in Probability Trees (FPT), a novel method that combines probabilistic trees and fuzzy logic. This approach is fully interpretable, providing clinicians with a tool generate and verify the entire clinical decision process.
methodsFPT extends the existing framework of Probabilistic Decision Trees by incorporating the uncertainty in the data, allowing for a flexible description of vague variables. Thus, FPTs enable the incorporation of domain knowledge in the form of fuzzy membership functions within the framework of probabilistic trees. Furthermore, FPTs can represent circumstances or explanations that cannot be represented with other techniques (e.g., Bayesian networks), paving the way to a novel form of interpretable AI that allows clinicians to generate, control and verify the entire diagnosis procedure; one of the strengths of our methodology is the capability to decrease the frequency of misdiagnoses by providing an estimate of uncertainties and counterfactuals.
resultsWe applied FPT to two real medical scenarios: classifying malignant thyroid nodules, and predicting the risk of progression in chronic kidney disease patients. Our results show that FPTs can provide interpretable support to clinicians. We also show that FPT and its predictions can assist clinical practice in an intuitive manner, with the use of a user-friendly interface specifically designed for this purpose.
conclusionThe integration of probabilistic trees and fuzzy reasoning preserves the nuances that are generally lost in (probabilistic) decision trees due to the adoption of crisp thresholds, leading to hybrid trees that provide an AI system better aligned with human reasoning processes and that can effectively support clinicians in the diagnosis decision process.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.