Bayesian Hybrid Machine Learning of Gallstone Risk

• URL: http://arxiv.org/abs/2506.14561v1
• Date: Tue, 17 Jun 2025 14:19:02 GMT
• Title: Bayesian Hybrid Machine Learning of Gallstone Risk
• Abstract summary: Gallstone disease is a complex, multifactorial condition with significant global health burdens.<n>We propose a hybrid machine learning framework that integrates robust variable selection with advanced interaction detection.<n>This proposed framework not only enhances prediction but also yields actionable insights, offering a valuable support tool for medical research and decision-making.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gallstone disease is a complex, multifactorial condition with significant global health burdens. Identifying underlying risk factors and their interactions is crucial for early diagnosis, targeted prevention, and effective clinical management. Although logistic regression remains a standard tool for assessing associations between predictors and gallstone status, it often underperforms in high-dimensional settings and may fail to capture intricate relationships among variables. To address these limitations, we propose a hybrid machine learning framework that integrates robust variable selection with advanced interaction detection. Specifically, Adaptive LASSO is employed to identify a sparse and interpretable subset of influential features, followed by Bayesian Additive Regression Trees (BART) to model nonlinear effects and uncover key interactions. Selected interactions are further characterized by physiological knowledge through differential equation-informed interaction terms, grounding the model in biologically plausible mechanisms. The insights gained from these steps are then integrated into a final logistic regression model within a Bayesian framework, providing a balance between predictive accuracy and clinical interpretability. This proposed framework not only enhances prediction but also yields actionable insights, offering a valuable support tool for medical research and decision-making.

Related papers

• Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement [81.27196033392559]
  We propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis.<n>COME injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training.<n>Our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline.
  arXiv  Detail & Related papers  (2026-07-24T04:26:47Z)
• Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer [8.562236981868866]
  Existing approaches often face a trade-off between model interpretability and representational capacity.<n>We report Pathway Activity Autoencoders for the multi-omics setting, which embed prior knowledge via pathway-informed architectural constraints.<n>Our framework is applied in the context of breast cancer and is evaluated in survival prediction and subtype classification.
  arXiv  Detail & Related papers  (2026-07-06T16:47:18Z)
• PliableBVS: A flexible Bayesian variable selection method for modeling interactions with mandatory modifying variables [1.0680378628718095]
  We introduce PliableBVS, a variable selection approach that preserves the hierarchical structure of the pliable lasso.<n>The proposed model combines the continuous shrinkage effect of Bayesian lasso with a hierarchical spike-and-slab prior formulation.<n>In simulation studies the proposed method outperforms the original pliable lasso in identifying active main and interaction effects.
  arXiv  Detail & Related papers  (2026-06-01T10:07:53Z)
• Joint Model and Data Sparsification via the Marginal Likelihood [53.29070892356214]
  We propose jointly learning individual feature and sample relevancies, enabling simultaneous model and data sparsification.<n>This symmetric pruning of model and data offers a natural extension that preserves conjugacy.<n> Empirical results across diverse regression tasks affirm that a joint ARD approach consistently yields both sparse and robust prediction models.
  arXiv  Detail & Related papers  (2026-05-28T13:26:53Z)
• Investigating the Impact of Histopathological Foundation Models on Regressive Prediction of Homologous Recombination Deficiency [52.50039435394964]
  We systematically evaluate foundation models for regression-based tasks.<n>We extract patch-level features from whole slide images (WSI) using five state-of-the-art foundation models.<n>Models are trained to predict continuous HRD scores based on these extracted features across breast, endometrial, and lung cancer cohorts.
  arXiv  Detail & Related papers  (2026-01-29T14:06:50Z)
• BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data [2.849014311160882]
  BaGGLS is a flexible and interpretable probabilistic binary regression model for high-dimensional biological inference involving feature interactions.<n>We show that BaGGLS is a promising approach for uncovering biologically relevant interaction patterns, with potential applicability across a range of high-dimensional tasks in computational biology.
  arXiv  Detail & Related papers  (2025-11-19T10:48:30Z)
• Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction [35.94354098982828]
  We introduce Hierarchical-CPI, a model-agnostic variable importance measure that frames the inference problem as the discovery of groups of variables.<n>By exploring subgroups along a hierarchical tree, it remains computationally tractable, yet also enjoys explicit family-wise error rate control.<n>Its effectiveness is demonstrated in two neuroimaging datasets.
  arXiv  Detail & Related papers  (2025-08-12T08:10:54Z)
• Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood [0.0]
  This study introduces an integrated framework for predictive causal inference designed to overcome limitations in conventional single model approaches.<n> Specifically, we combine a Hidden Markov Model for spatial health state estimation with a Multi Task and Multi Graph Convolutional Network (MTGCN) for capturing temporal outcome trajectories.<n>To demonstrate its utility, we focus on clinical domains such as cancer, dementia, Parkinson disease, where treatment effects are challenging to observe directly.
  arXiv  Detail & Related papers  (2025-07-11T03:11:15Z)
• Information-theoretic Quantification of High-order Feature Effects in Classification Problems [0.19791587637442676]
  We present an information-theoretic extension of the High-order interactions for Feature importance (Hi-Fi) method.<n>Our framework decomposes feature contributions into unique, synergistic, and redundant components.<n>Results indicate that the proposed estimator accurately recovers theoretical and expected findings.
  arXiv  Detail & Related papers  (2025-07-06T11:50:30Z)
• A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example [2.325005809983534]
  This paper introduces a symbolic and statistical learning framework to identify key regulatory mechanisms and model uncertainty.<n>A Metropolis-adjusted Langevin algorithm with adjoint sensitivity analysis is developed for posterior exploration.<n>An empirical study demonstrates its ability to recover missing regulatory mechanisms and improve model fidelity under datalimited conditions.
  arXiv  Detail & Related papers  (2025-05-06T04:39:34Z)
• Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction [40.96453902709292]
  We propose pretraining strategies that leverage a phenomenon in real-world applications.<n>In medicine, components of the same biological signaling pathways frequently influence both baseline risk and treatment response.<n>We use this structure to incorporate "side information" and develop models that can exploit synergies between risk prediction and causal effect estimation.
  arXiv  Detail & Related papers  (2025-05-01T05:12:14Z)
• Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data [18.539194412343104]
  This study introduces the Probabilistic Causal Fusion (PCF) framework.<n>PCF integrates Causal Bayesian Networks (CBNs) and Probability Trees (PTrees) to extend beyond predictions.<n>It was validated on three real-world healthcare datasets.
  arXiv  Detail & Related papers  (2025-01-27T11:34:19Z)
• Causal Representation Learning from Multimodal Biomedical Observations [57.00712157758845]
  We develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets.<n>Key theoretical contribution is the structural sparsity of causal connections between modalities.<n>Results on a real-world human phenotype dataset are consistent with established biomedical research.
  arXiv  Detail & Related papers  (2024-11-10T16:40:27Z)
• Jacobian Regularizer-based Neural Granger Causality [45.902407376192656]
  We propose a Jacobian Regularizer-based Neural Granger Causality (JRNGC) approach. Our method eliminates the sparsity constraints of weights by leveraging an input-output Jacobian matrix regularizer. Our proposed approach achieves competitive performance with the state-of-the-art methods for learning summary Granger causality and full-time Granger causality.
  arXiv  Detail & Related papers  (2024-05-14T17:13:50Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction [50.7901190642594]
  We propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner. It maintains consistent and robust semantics by smoothing relations around the target interaction.
  arXiv  Detail & Related papers  (2023-12-09T07:08:00Z)
• Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications [60.26261850082012]
  We introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding. We employ association rules as new representations using association rule mining based on the original features to increase model interpretability.
  arXiv  Detail & Related papers  (2022-09-29T17:44:14Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data [7.422597776308963]
  We propose a tree-guided feature selection and logic aggregation approach for large-scale regression with rare binary features. In a suicide risk study with EHR data, our approach is able to select and aggregate prior mental health diagnoses.
  arXiv  Detail & Related papers  (2022-06-18T03:52:43Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.