Joseph Arul Raj

Linglong Qian, Joseph Arul Raj, Hugh Logan Ellis et al.

We introduce the Conditional Self-Attention Imputation (CSAI) model, a novel recurrent neural network architecture designed to address the challenges of complex missing data patterns in multivariate time series derived from hospital electronic health records (EHRs). CSAI extends state-of-the-art neural network-based imputation by introducing key modifications specific to EHR data: a) attention-based hidden state initialisation to capture both long- and short-range temporal dependencies prevalent in EHRs, b) domain-informed temporal decay to mimic clinical data recording patterns, and c) a non-uniform masking strategy that models non-random missingness by calibrating weights according to both temporal and cross-sectional data characteristics. Comprehensive evaluation across four EHR benchmark datasets demonstrates CSAI's effectiveness compared to state-of-the-art architectures in data restoration and downstream tasks. CSAI is integrated into PyPOTS, an open-source Python toolbox designed for machine learning tasks on partially observed time series. This work significantly advances the state of neural network imputation applied to EHRs by more closely aligning algorithmic imputation with clinical realities.

Joseph Arul Raj, Linglong Qian, Zina Ibrahim

Secondary research use of Electronic Health Records (EHRs) is often hampered by the abundance of missing data in this valuable resource. Missingness in EHRs occurs naturally as a result of the data recording practices during routine clinical care, but handling it is crucial to the precision of medical analysis and the decision-making that follows. The literature contains a variety of imputation methodologies based on deep neural networks. Those aim to overcome the dynamic, heterogeneous and multivariate missingness patterns of EHRs, which cannot be handled by classical and statistical imputation methods. However, all existing deep imputation methods rely on end-to-end pipelines that incorporate both imputation and downstream analyses, e.g. classification. This coupling makes it difficult to assess the quality of imputation and takes away the flexibility of re-using the imputer for a different task. Furthermore, most end-to-end deep architectures tend to use complex networks to perform the downstream task, in addition to the already sophisticated deep imputation network. We, therefore ask if the high performance reported in the literature is due to the imputer or the classifier and further ask if an optimised state-of-the-art imputer is used, a simpler classifier can achieve comparable performance. This paper explores the development of a modular, deep learning-based imputation and classification pipeline, specifically built to leverage the capabilities of state-of-the-art imputation models for downstream classification tasks. Such a modular approach enables a) objective assessment of the quality of the imputer and classifier independently, and b) enables the exploration of the performance of simpler classification architectures using an optimised imputer.