5 papers
Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Tasks
Joseph Arul Raj, Linglong Qian, Zina Ibrahim
Missing values are pervasive in large-scale time-series data, posing challenges for reliable analysis and decision-making. Many neural architectures have been designed to model and…
CSAI: Conditional Self-Attention Imputation for Healthcare Time-series
Linglong Qian, Joseph Arul Raj, Hugh Logan Ellis +5
We introduce the Conditional Self-Attention Imputation (CSAI) model, a novel recurrent neural network architecture designed to address the challenges of complex missing data patter…
METHOD: Modular Efficient Transformer for Health Outcome Discovery
Linglong Qian, Zina Ibrahim
Recent advances in transformer architectures have revolutionised natural language processing, but their application to healthcare domains presents unique challenges. Patient timeli…
How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation
Linglong Qian, Tao Wang, Jun Wang +4
We present a comprehensive analysis of deep learning approaches for Electronic Health Record (EHR) time-series imputation, examining how architectural and framework biases combine…
Beyond Random Missingness: Clinically Rethinking for Healthcare Time Series Imputation
Linglong Qian, Yiyuan Yang, Wenjie Du +3
This study investigates the impact of masking strategies on time series imputation models in healthcare settings. While current approaches predominantly rely on random masking for…