21 citations · 31 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
TSI-Bench: Benchmarking Time Series Imputation
Wenjie Du, Jun Wang, Linglong Qian +12
Effective imputation is a crucial preprocessing step for time series analysis. Despite the development of numerous deep learning algorithms for time series imputation, the communit…
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…
Question answering systems for health professionals at the point of care -- a systematic review
Gregory Kell, Angus Roberts, Serge Umansky +6
Objective: Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence.…
Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation
Linglong Qian, Zina Ibrahim, Richard Dobson
Missingness is ubiquitous in multivariate time series and poses an obstacle to reliable downstream analysis. Although recurrent network imputation achieved the SOTA, existing model…