Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes
Not on the map. Why?
Abstract (arXiv)
Diabetes, a pervasive and enduring health challenge, imposes significant global implications on health, financial healthcare systems, and societal well-being. This study undertakes a comprehensive exploration of various structural learning algorithms to discern causal pathways amongst potential risk factors influencing diabetes progression. The methodology involves the application of these algorithms to relevant diabetes data, followed by the conversion of their output graphs into Causal Bayesian Networks (CBNs), enabling predictive analysis and the evaluation of discrepancies in the effect of hypothetical interventions within our context-specific case study. This study highlights the substantial impact of algorithm selection on intervention outcomes. To consolidate insights from diverse algorithms, we employ a model-averaging technique that helps us obtain a unique causal model for diabetes derived from a varied set of structural learning algorithms. We also investigate how each of those individual graphs, as well as the average graph, compare to the structures elicited by a domain expert who categorised graph edges into high confidence, moderate, and low confidence types, leading into three individual graphs corresponding to the three levels of confidence. The resulting causal model and data are made available online, and serve as a valuable resource and a guide for informed decision-making by healthcare practitioners, offering a comprehensive understanding of the interactions between relevant risk factors and the effect of hypothetical interventions. Therefore, this research not only contributes to the academic discussion on diabetes, but also provides practical guidance for healthcare professionals in developing efficient intervention and risk management strategies.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 107 references that are in the map. First posted on arXiv 21 Mar 2024.
Builds on
16 of 107 references.
References · 16
- 2019Gradient-Based Neural DAG Learningcited by 227
- 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
- 2019Learning Sparse Nonparametric DAGscited by 207
- 2020Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Datacited by 85
- 2021A survey of Bayesian Network structure learningcited by 85
- 2020A Tutorial on Learning With Bayesian Networkscited by 56
- 2023A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnosticscited by 42
- 2013The Bayesian Structural EM Algorithmcited by 26
- 2016Condensed Memory Networks for Clinical Diagnostic Inferencingcited by 14
- 2017Detecting confounding in multivariate linear models via spectral analysiscited by 14
- 2015A hybrid algorithm for Bayesian network structure learning with application to multi-label learningcited by 12
- 2019Evaluating structure learning algorithms with a balanced scoring functioncited by 10
4 more
- 2020Learning Bayesian Networks that enable full propagation of evidencecited by 7
- 2021Effective and efficient structure learning with pruning and model averaging strategiescited by 7
- 2023Open problems in causal structure learning: A case study of COVID-19 in the UKcited by 7
- 2019Learning Bayesian networks from demographic and health survey datacited by 6
Built on by
Citing papers per year: 2025: 1.
1 paper.