6 citations · 19 across the 9 of their papers we have counts for
7 papers · 1 filter
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…
Assessing the risk of re-identification arising from an attack on anonymised data
Anna Antoniou, Giacomo Dossena, Julia MacMillan +3
Objective: The use of routinely-acquired medical data for research purposes requires the protection of patient confidentiality via data anonymisation. The objective of this work is…
Privacy-aware Early Detection of COVID-19 through Adversarial Training
Omid Rohanian, Samaneh Kouchaki, Andrew Soltan +4
Early detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational stra…