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

Area of Science:

• Biostatistics
• Machine Learning in Medicine
• Clinical Epidemiology

Background:

• Clinical prediction models face a trade-off between generalizability (global models) and individualization (personalized models).
• Identifying patient subgroups that deviate from global model predictions is crucial for improving clinical decision-making.

Purpose of the Study:

• To propose a diagnostic tool for contrasting global and patient-specific regression models.
• To identify patient subgroups inadequately represented by global models.
• To characterize these subgroups and understand deviations from global predictions.

Main Methods:

• Development of a localized regression approach to identify regions of inadequacy in the predictor space.
• Utilizing an autoencoder for dimension reduction to create a latent representation for modeling with many predictors.
• Simultaneous optimization of data reconstruction and local outcome associations within the latent space.

Main Results:

• The proposed tool effectively identifies subgroups benefiting from personalized models.
• Global models were found adequate for most patients, but specific subgroups showed significant deviations.
• Mapping back to original predictors provided insights into the reasons for global model inadequacy in certain groups.

Conclusions:

• The diagnostic tool aids in deciding between global and personalized clinical prediction models.
• It enables the identification and characterization of patient subgroups requiring tailored approaches.
• This approach enhances the precision and applicability of clinical prediction models.