Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering

In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical decision-making. However, clustering in healthcare informatics is still based on traditional methods, especially K-means, and has achieved limited success when applied to embedding representations learned by autoencoders as hybrid methods. This paper investigates the effectiveness of traditional, hybrid, and deep learning methods in heart failure patient cohorts using real EHR data from the All of Us Research Program. Traditional clustering methods perform robustly because deep learning approaches are specifically designed for image clustering, a task that differs substantially from the tabular EHR data setting. To address the shortcomings of deep clustering, we introduce an ensemble-based deep clustering approach that aggregates cluster assignments obtained from multiple embedding dimensions, rather than relying on a single fixed embedding space. When combined with traditional clustering in a novel ensemble framework, the proposed ensemble embedding for deep clustering delivers the best overall performance ranking across 14 diverse clustering methods and multiple patient cohorts. This paper underscores the importance of biological sex-specific clustering of EHR data and the advantages of combining traditional and deep clustering approaches over a single method.

DeepIFSA: Deep Imputation of Missing Values Using Feature and Sample Attention

Missing values of varying patterns and rates in real-world tabular data pose a significant challenge in developing reliable data-driven models. Existing missing value imputation methods use statistical and traditional machine learning, which are ineffective when the missing rate is high and not at random. This paper explores row and column attention in tabular data to address the shortcomings of existing methods by introducing a new method for imputing missing values. The method combines between-feature and between-sample attention learning in a deep data reconstruction framework. The proposed data reconstruction uses CutMix data augmentation within a contrastive learning framework to improve the uncertainty of missing value estimation. The performance and generalizability of trained imputation models are evaluated on set-aside test data folds with missing values. The proposed joint attention learning outperforms nine state-of-the-art imputation methods across several missing value types and rates (10%-50%) on twelve data sets. Real electronic health records data with missing values yield the best classification accuracy when imputed using the proposed attention learning compared to other statistical, machine learning, and deep imputation methods. This paper highlights the heterogeneity of tabular data sets to recommend imputation methods based on missing value types and data characteristics.