ArticleCentral European journal of operations research2018
A framework for sensitivity analysis of decision trees.
Article in Central European journal of operations research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 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
64 citing papers in PubMed.
- A Decision Tree Analysis on Symptom Experience of Asian American Breast Cancer Survivors.Western journal of nursing research · 2023Trial
- Information Needs of Asian American Breast Cancer Survivors: a Decision Tree Analysis.Journal of cancer education : the official journal of the American Association for Cancer Education · 2022Trial
- A Decision Tree Analysis on the Impact of a Technology-Based Program on Symptom Distress: Asian American Breast Cancer Survivors.Computers, informatics, nursing : CIN · 2022Trial
- Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.PloS one · 2021Trial
- Operational mechanisms and application advances in artificial olfactory systems.Biomedical engineering online · 2026Review
- Cross-lingual SMS spam detection using GAN-based augmentation for imbalanced datasets.Scientific reports · 2026Article
- Article
- Comparison of machine learning methods in forecasting and characterizing the birch and grass pollen season.PloS one · 2026Article
- Analysis of pathogen distribution and risk factors for extended antibiotic course in children with microbiological-based protracted bacterial bronchitis in southwest China.Frontiers in pediatrics · 2026Article
- Analysis of factors affecting the academic performance of university students using machine learning.Scientific reports · 2025Article
- Dosing prediction of valproic acid in pediatric patients with epilepsy: population pharmacokinetic model or machine learning model?European journal of clinical pharmacology · 2025Article
- Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation.Scientific reports · 2025Article
- Non-invasive identification of TKI-resistant NSCLC: a multi-model AI approach for predicting EGFR/TP53 co-mutations.BMC pulmonary medicine · 2025Article
- Cost-Effectiveness of Intensive Blood Pressure Control in Youth With Chronic Kidney Disease.Hypertension (Dallas, Tex. : 1979) · 2025Article
- A new method for assessing the health status of aerospace equipment based on a belief rule base with balanced accuracy and complexity.Scientific reports · 2025Article
- Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Predicting depression in patients with heart failure based on a stacking model.World journal of clinical cases · 2024Article
- Article
- The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study.BMC oral health · 2024Article
- Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal.Sensors (Basel, Switzerland) · 2024Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.
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.