73 citations · 155 across the 19 of their papers we have counts for
20 papers
All models are local: time to replace external validation with recurrent local validation
Alex Youssef, Michael Pencina, Anshul Thakur +3
External validation is often recommended to ensure the generalizability of ML models. However, it neither guarantees generalizability nor equates to a model's clinical usefulness (…
Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention
Anshul Thakur, Tingting Zhu, Vinayak Abrol +3
The lack of data democratization and information leakage from trained models hinder the development and acceptance of robust deep learning-based healthcare solutions. This paper ar…
Medical records condensation: a roadmap towards healthcare data democratisation
Yujiang Wang, Anshul Thakur, Mingzhi Dong +5
The prevalence of artificial intelligence (AI) has envisioned an era of healthcare democratisation that promises every stakeholder a new and better way of life. However, the advanc…
Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Taha Ceritli, Ghadeer O. Ghosheh, Vinod Kumar Chauhan +3
Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data. Synthetic data generation is a promising solution to…
Adversarial De-confounding in Individualised Treatment Effects Estimation
Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania +3
Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the…
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
Ghadeer Ghosheh, Jin Li, Tingting Zhu
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede…
Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications
Jin Li, Benjamin J. Cairns, Jingsong Li +1
The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has be…
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning
Rasheed el-Bouri, Tingting Zhu, David A. Clifton
Given the abundance and ease of access of personal data today, individual privacy has become of paramount importance, particularly in the healthcare domain. In this work, we aim to…
DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time
Girmaw Abebe Tadesse, Hamza Javed, Yong Liu +4
Myocardial Infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). Detection of MI and information regarding its occurrence-time in particular, would enabl…
Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals
Dani Kiyasseh, Tingting Zhu, David Clifton
Cardiac signals, such as the electrocardiogram, convey a significant amount of information about the health status of a patient which is typically summarized by a clinician in the…
PCPs: Patient Cardiac Prototypes
Dani Kiyasseh, Tingting Zhu, David A. Clifton
Many clinical deep learning algorithms are population-based and difficult to interpret. Such properties limit their clinical utility as population-based findings may not generalize…
Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease
Henrique Aguiar, Mauro Santos, Peter Watkinson +1
Recent years have seen an increased focus into the tasks of predicting hospital inpatient risk of deterioration and trajectory evolution due to the availability of electronic patie…
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age
Dani Kiyasseh, Tingting Zhu, David A. Clifton
The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks. Such tasks include disease…
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
Rasheed el-Bouri, David Eyre, Peter Watkinson +2
Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing wit…
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
Dani Kiyasseh, Tingting Zhu, David A. Clifton
The healthcare industry generates troves of unlabelled physiological data. This data can be exploited via contrastive learning, a self-supervised pre-training method that encourage…
SoQal: Selective Oracle Questioning in Active Learning
Dani Kiyasseh, Tingting Zhu, David A. Clifton
Large sets of unlabelled data within the healthcare domain remain underutilized. Active learning offers a way to exploit these datasets by iteratively requesting an oracle (e.g. me…
CLOPS: Continual Learning of Physiological Signals
Dani Kiyasseh, Tingting Zhu, David A. Clifton
Deep learning algorithms are known to experience destructive interference when instances violate the assumption of being independent and identically distributed (i.i.d). This viola…
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals
Dani Kiyasseh, Tingting Zhu, David A. Clifton
Clinical settings are often characterized by abundant unlabelled data and limited labelled data. This is typically driven by the high burden placed on oracles (e.g., physicians) to…
Severity Detection Tool for Patients with Infectious Disease
Girmaw Abebe Tadesse, Tingting Zhu, Nhan Le Nguyen Thanh +14
Hand, foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low and middle income countries. Tetanus in particular has a high mortality rate and its treatmen…
Fusing Continuous-valued Medical Labels using a Bayesian Model
Tingting Zhu, Nic Dunkley, Joachim Behar +2
With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels i…