🤖 AI Summary
This study addresses the performance bottleneck in personalized prediction caused by inaccurate patient similarity measures. We propose a supervised weighted cosine similarity method based on relaxed adaptive group Lasso, which leverages supervised learning to adaptively estimate feature weights, thereby overcoming the limitations of traditional unsupervised similarity computations. This approach significantly enhances personalized modeling for binary classification data. Experiments on ICU datasets demonstrate that the proposed model effectively improves discriminative power and overall predictive accuracy, as evidenced by improved Brier scores. Although calibration exhibits a marginal decline, this work establishes a novel paradigm for precise clinical prediction by integrating supervision into similarity metric learning.
📝 Abstract
Personalized predictive modelling has been growing rapidly with the increasing availability of Electronic Health Records. This approach aims to improve a model's predictive performance by fitting a unique model to each individual. We train the model on a subset of the training data consisting of individuals similar to the individual being predicted, identified through some similarity metric. Earlier studies show that using a personalized model trained on a customized subset of the data leads to better prediction than using a global model trained on the full dataset. In this work, we develop a new patient similarity metric to improve the prediction of a personalized model for binary response data. Specifically, we introduce a weighted cosine similarity metric that extends the standard cosine similarity metric by assigning predictor-specific weights when computing similarity between participants. These weights are estimated using a supervised approach with the relaxed adaptive group lasso. Results from simulation studies and an analysis of intensive care unit data show that although our proposed similarity metric leads to a slight deterioration in calibration, it produces substantial gains in discrimination. Overall predictive performance measured by the Brier Score improves because the increase in discrimination outweighs the loss in calibration; therefore, our proposed similarity metric more effectively identifies similar participants, resulting in improved predictive accuracy.