Digital Health
Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit
Villarroel M,  Chaichulee S,  Jorge J,  Davis S,  Green G,  Arteta C,  Zisserman A,  McCormick K,  Watkinson P,  Tarassenko L,  et al. (2019)
Adversarial de-confounding in individualised treatment effects estimation
Kumar V,  Molaei S,  Hoque Tania M,  Thakur A,  Zhu T,  Clifton D,  et al. (2023)
Retrieve, reason, and refine: generating accurate and faithful patient instructions
Liu F,  Yang B,  You C,  Wu X,  Ge S,  Liu Z,  Sun X,  Yang Y,  Clifton D,  et al. (2023)
Expectation-maximization contrastive learning for compact video-and-language representations
Jin P,  Huang J,  Liu F,  Wu X,  Ge S,  Song G,  Clifton D,  Chen J,  et al. (2023)
Privacy-aware early detection of COVID-19 through adversarial training
Rohanian M,  Kouchaki S,  Soltan A,  Yang J,  Yang Y,  Clifton D,  et al. (2022)
Digital health and machine learning technologies for blood glucose monitoring and management of gestational diabetes
Lu H,  Ding X,  Hirst J,  Yang Y,  Yang J,  Mackillop L,  Clifton D,  et al. (2023)
Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England
Hill NR,  Groves L,  Dickerson C,  Ochs A,  Pang D,  Lawton S,  Hurst M,  Pollock KG,  Sugrue DM,  Tsang C,  Arden C,  Wyn Davies D,  Martin AC,  Sandler B,  Gordon J,  Farooqui U,  Clifton D,  Mallen C,  Rogers J,  Camm AJ,  Cohen AT,  et al. (2022)
Self-aware SGD: reliable incremental adaptation framework for clinical AI models
Thakur A,  Armstrong J,  Youssef A,  Eyre D,  Clifton DA,  et al. (2023)
Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank
Small S,  Chan S,  Walmsley R,  Fritsch LV,  Acquah A,  Mertes G,  Feakins B,  Creagh A,  Strange A,  Matthews C,  Clifton D,  Price A,  Khalid S,  Bennett D,  Doherty A,  et al. (2023)
On the effectiveness of compact biomedical transformers
Rohanian O,  Nouriborji M,  Kouchaki S,  Clifton DA,  et al. (2023)
Heterogeneity in the diagnosis and prognosis of ischemic stroke subtypes: 9-year follow-up of 22,000 cases in Chinese adults
Chun M,  Qin H,  Turnbull I,  Sansome S,  Gilbert S,  Hacker A,  Wright N,  Zhu T,  Clifton D,  Bennett D,  Guo Y,  Pei P,  Lv J,  Yu C,  Yang L,  Li L,  Lu Y,  Chen Z,  Cairns BJ,  Chen Y,  Clarke R,  et al. (2023)
Patient clustering for vital organ failure using ICD code with graph attention
Liu Z,  Hu Y,  Wu X,  Mertes G,  Yang Y,  Clifton DA,  et al. (2023)
An adversarial training framework for mitigating algorithmic biases in clinical machine learning
Yang J,  Soltan AAS,  Eyre DW,  Yang Y,  Clifton DA,  et al. (2023)
Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis
Sonnenkalb L,  Carter JJ,  Spitaleri A,  Iqbal Z,  Hunt M,  Malone KM,  Utpatel C,  Cirillo DM,  Rodrigues C,  Nilgiriwala KS,  Fowler PW,  Merker M,  Niemann S,  et al. (2023)
SMKD: Selective mutual knowledge distillation
Li Z,  Wang X,  Robertson NM,  Clifton D,  Meinel C,  Yang H,  et al. (2023)
Uncertainties in the analysis of heart rate variability: a systematic review
Lu L,  Zhu T,  Morelli D,  Creagh A,  Liu Z,  Yang J,  Liu F,  Zhang Y-T,  Clifton D,  et al. (2023)
Scalable federated learning for emergency care using low cost microcomputing: Real-world, privacy preserving development and evaluation of a COVID-19 screening test in UK hospitals
Soltan A,  Thakur A,  Yang J,  Chauhan A,  D’Cruz L,  Dickson P,  Soltan M,  Thickett D,  Eyre D,  Zhu T,  Clifton D,  et al. (2023)
Dynamic inter-treatment information sharing for heterogeneous treatment effects estimation
Chauhan VK,  Zhou J,  Molaei S,  Ghosheh G,  Clifton DA,  et al. (2023)
Combining machine learning with Cox models to identify predictors for incident post-menopausal breast cancer in the UK Biobank
Liu X,  Morelli D,  Littlejohns J T,  Clifton A D,  Clifton L,  et al. (2023)
MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers
Nouriborji M,  Rohanian O,  Kouchaki S,  Clifton DA,  et al. (2023)
Evaluation of awake prone positioning effectiveness in moderate to severe COVID-19
Truong NT,  Phong NT,  Nguyen NT,  Khanh LTT,  Tran LHB,  Linh NTM,  Thao DP,  Trinh NTD,  Kieu PT,  Thao NTP,  Hoang VT,  Ngoc NT,  Oanh PKN,  Vien TTD,  Tung NLN,  Ly VT,  Khoa TD,  Phu NH,  Van CTC,  Duc TM,  Beane A,  Khoa LDV,  Clifton D,  Kestelyn E,  Hai HB,  Yen LM,  Tan LV,  Glover G,  Thwaites G,  Geskus R,  Duc DH,  Dung NT,  Thwaites L,  et al. (2023)
A Brief Review of Hypernetworks in Deep Learning
Chauhan VK,  Zhou J,  Lu P,  Molaei S,  Clifton D,  et al. (2023)
DyVGRNN: DYnamic mixture variational graph recurrent neural networks
Niknam G,  Molaei S,  Zare H,  Pan S,  Jalili M,  Zhu T,  Clifton D,  et al. (2023)
Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit
Nhat PTH,  Van Hao N,  Tho PV,  Kerdegari H,  Pisani L,  Thu LNM,  Phuong LT,  Duong HTH,  Thuy DB,  McBride A,  Xochicale M,  Schultz MJ,  Razavi R,  King AP,  Thwaites C,  Van Vinh Chau N,  Yacoub S,  Gomez A,  et al. (2023)
Independent external validation of the QRISK3 cardiovascular disease risk prediction model using UK Biobank
Parsons RE,  Liu X,  Collister JA,  Clifton DA,  Cairns BJ,  Clifton L,  et al. (2023)
Improving diagnostics with deep forest applied to electronic health records
Khodadadi A,  Ghanbari Bousejin N,  Molaei S,  Kumar Chauhan V,  Zhu T,  Clifton D,  et al. (2023)