Patient similarity learning aims to derive a clinically meaningful similarity metric to measure the similarity between a pair of patients according to their historical clinical information, which could help to predict the clinical outcomes of the patient of interest. However, the patient clinical data are usually complex, and contain much irrelevant and redundant information, which makes it difficult to learn the similarity metric with high accuracy. Although some methods have been proposed to address the complex nature of patient data, they overemphasize sparsity-based relevant feature selection and fail to take into consideration the redundant features that are highly correlated with each other, and this heavily degrades the accuracy of the learned results. To address the above challenges, we propose a novel uncorrelated patient similarity learning approach, which can not only select the most relevant features for the learning task, but also guarantee that the selected features have low correlations with each other. Additionally, to address the scenarios where the patient data are distributed across different sites, we extend the proposed approach and design a distributed mechanism, based on which the similarity metric can be accurately learned without directly accessing the raw patient data at each site. The desirable performance of the proposed methods are verified through extensive experiments conducted on both real-world and synthetic datasets.
In REF , a method is proposed to learn a metric which can be used to learn the patient similarity based on a complex medical record.
Uncorrelated Patient Similarity Learning
SDM
Computer Science