Contrasting Global and Patient-Specific Regression Models via a Neural Network Representation

2026-01-26

AI Summary

This work proposes an interpretable diagnostic framework to address the limitations of global models in clinical prediction, which often fail to account for individual heterogeneity and lack mechanisms to identify patient subgroups where they underperform. By jointly optimizing an autoencoder’s reconstruction capability and its association with local outcomes, the method constructs a locally weighted regression model in a low-dimensional latent space. It identifies regions where the global model performs poorly by comparing global and patient-specific predictions. Crucially, the approach enables mapping discovered subgroups back to the original feature space to elucidate the underlying failure mechanisms. Validation in a chronic obstructive pulmonary disease cohort demonstrates that while the global model suffices for most patients, a distinct subgroup significantly benefits from personalized modeling, with key clinical characteristics driving this differential performance successfully uncovered.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Local Search
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

When developing clinical prediction models, it can be challenging to balance between global models that are valid for all patients and personalized models tailored to individuals or potentially unknown subgroups. To aid such decisions, we propose a diagnostic tool for contrasting global regression models and patient-specific (local) regression models. The core utility of this tool is to identify where and for whom a global model may be inadequate. We focus on regression models and specifically suggest a localized regression approach that identifies regions in the predictor space where patients are not well represented by the global model. As localization becomes challenging when dealing with many predictors, we propose modeling in a dimension-reduced latent representation obtained from an autoencoder. Using such a neural network architecture for dimension reduction enables learning a latent representation simultaneously optimized for both good data reconstruction and for revealing local outcome-related associations suitable for robust localized regression. We illustrate the proposed approach with a clinical study involving patients with chronic obstructive pulmonary disease. Our findings indicate that the global model is adequate for most patients but that indeed specific subgroups benefit from personalized models. We also demonstrate how to map these subgroup models back to the original predictors, providing insight into why the global model falls short for these groups. Thus, the principal application and diagnostic yield of our tool is the identification and characterization of patients or subgroups whose outcome associations deviate from the global model.

Problem

• global regression models
• patient-specific models
• localized regression
• clinical prediction
• model inadequacy

Innovation

• localized regression
• autoencoder
• neural network representation
• global vs. patient-specific models
• latent representation