21 citations · 31 across the 11 of their papers we have counts for
12 papers
Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment
Pawel Renc, Michal K. Grzeszczyk, Linglong Qian +3
We present Federated Timeline Synthesis (FTS), a novel framework for training generative foundation models across distributed timeseries data applied to electronic health records (…
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…
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…
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…
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model
Yuezhou Zhang, Amos A Folarin, Judith Dineley +25
Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processi…
Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings
Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun +27
Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of dai…
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
Zina M Ibrahim, Daniel Bean, Thomas Searle +7
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome predic…