Computational Health Informatics lab journal articles
Journal articles
[228] Nhan, L.N.T., ..., Clifton, D.A., …, Thwaites, C.L.:
Feasibility of Wearable Monitors to Detect Heart Rate Variability in Children with Hand, Foot and Mouth Disease
BMC Infectious Diseases, 2024, 24 (1), 1-7
[227] Meeraus, W., Clifton, D.A., …, de Lusignan, S.:
AZD1222 Effectiveness Against Severe COVID-19 in Individuals with Comorbidity or Frailty: The RAVEN Cohort Study
Journal of Infection, 2024, 106129
[226] Yang, B., Liu, F., ..., Clifton, D.A.:
Zeronlg: Aligning and Autoencoding Domains for Zero-shot Multimodal and Multilingual Natural Language Generation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
[225] Thakur, A., Zhu, T., …, Armstrong, J., …, Clifton, D.A.:
Data Encoding for Healthcare Data Democratization and Information Leakage Prevention
Nature Communications, 2024, 15 (1), 1582
[224] Lu, L., Zhu, T., ..., Clifton, D.A.:
Decoding 2.3 Million ECGs: Interpretable Deep Learning for Advancing Cardiovascular Diagnosis and Mortality Risk Stratification
European Heart Journal-Digital Health, 2024, ztae014
[223] You, C., …, Liu, F., Clifton, D.A., …, Duncan. J.:
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-reduction Perspective
Advances in Neural Information Processing Systems, 2024, 36
[222] Creagh, A.P., …, Mertes, G., ..., Clifton, D.A.:
Digital Health Technologies and Machine Learning Augment Patient Reported Outcomes to Remotely Characterise Rheumatoid Arthritis
NPJ Digital Medicine, 2024, 7 (1), 33
[221] Soltan, A.A.S., Thakur, A., Yang, J., ..., Zhu, T., Clifton, D.A.:
A Scalable Federated Learning Solution for Secondary Care Using Low-cost Microcomputing: Privacy-Preserving Development and Evaluation of a COVID-19 Screening Test in UK Hospitals
The Lancet Digital Health, 2024, 6 (2), e93-e104
[220] Lu, P., …, VITAL Consortium, ..., Clifton, D.A.:
Tetanus Severity Classification in Low-Middle Income Countries through ECG Wearable Sensors and a 1D-Vision Transformer
BioMedInformatics, 2024, 4 (1), 285-294
[219] Pollock, K.G., ..., Clifton, D.A., …, Cohen, A.T.:
Undertaking Multi-centre Randomised Controlled Trials in Primary Care: Learnings and Recommendations from The Pulse-AI Trial Researchers
BMC Primary Care, 2024, 25 (1), 7
[218] Hai, H.B., ..., Lu, P., Greeff, H., Zhu, T., …, Clifton, D.A., Thwaites, C.L.:
Heart Rate Variability Measured from Wearable Devices as a Marker of Disease Severity in Tetanus
The American Journal of Tropical Medicine and Hygiene, 2024, 110 (1), 165
[217] Liu, Z., Zhu, T., Lu, L., …, Clifton, D.A.:
Intelligent Electrocardiogram Acquisition via Ubiquitous Photoplethysmography Monitoring
IEEE Journal of Biomedical and Health Informatics, 2023
[216] Tania, M.H., Clifton, D.A.:
Unleashing the Power of Federated Learning in Fragmented Digital Healthcare Systems: A Visionary Perspective 2023
15th International Conference on Software, Knowledge, Information, 2023
[215] Liu, F., Zhu, T., …, Wang, C., Lu, L., ..., Clifton, D.A.:
A Medical Multimodal Large Language Model for Future Pandemics
NPJ Digital Medicine, 2023, 6 (1), 226 5
[214] Rohanian, M., …, Rohanian, O., Clifton, D.A., Krauthammer, M.:
Disfluent Cues for Enhanced Speech Understanding in Large Language Models
The 2023 Conference on Empirical Methods in Natural Language Processing, 2023
[213] Liu, Z., …, Clifton, D.A.:
DuKA: A Dual-Keyless-Attention Model for Multi-modality EHR Data Fusion and Organ Failure Prediction
IEEE Transactions on Biomedical Engineering, 2023
[212] Zhu, T., Lu, L., Clifton, D.A.:
The 2023 Wearable Photoplethysmography Roadmap: Hospital Monitoring
Physiological Measurement, 2023
[211] Charlton, P.H., ..., Clifton, D.A., …, Lu, L., Zhu, T.:
The 2023 Wearable Photoplethysmography Roadmap
Physiological measurement, 2023, 44 (11), 111001
[210] Yang, J., …, Lachapelle, A.S., Soltan, A.A.S., ..., Lu, L., Clifton, D.A.:
Deep Reinforcement Learning for Multi-Class Imbalanced Training: Applications in Healthcare
Machine Learning, 2023, 1-20
[209] Yang, J., …, Lu, L., Clifton, D.A.:
Interpretable Machine Learning-Based Decision Support for Prediction of Antibiotic Resistance for Complicated Urinary Tract Infections
NPJ Antimicrobials and Resistance, 2023, 1 (1), 14
[208] Youssef, A., …, Thakur, A., Zhu, T., Clifton, D.A., Shah, N.H.:
External Validation of AI Models in Health should be replaced with Recurring Local Validation
Nature Medicine, 2023, 29 (11), 2686-2687
[207] Parsons, R.E., …, Clifton, D.A., …, Clifton, L.:
Independent External Validation of the QRISK3 Cardiovascular Disease Risk Prediction Model using UK Biobank
Heart, 2023, 109 (22), 1690-1697
[206] Lu, H.Y., Lu, P., …, Clifton, D.A.:
A Stacked Long Short-term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus
Sensors, 2023, 23 (18), 7990
[205] Lu, P., Creagh, A.P., Lu, H.Y., …, Clifton, D.A.:
2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low-and Middle-Income Countries
Sensors, 2023, 23 (18), 7705
[204] Xiang, T., …, Lu, L., Clifton, D.A., ..., Zhang, Y.T.:
Towards Wearable Sensing-Based Precise and Rapid Responding System for the Early Detection of Future Pandemic
Connected Health and Telemedicine, 2023
[203] Yang, J., Soltan, A.A.S., …, Clifton, D.A.:
Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning with Deep Reinforcement Learning
Nature Machine Intelligence, 2023, 5 (8), 884-894
[202] Niknam, G., Molaei, S., …, Zhu, T., Clifton, D.A.:
DyVGRNN: DYnamic Mixture Variational Graph Recurrent Neural Networks
Neural Networks, 2023, 165, 596-610
[201] Khodadadi, A., …, Molaei, S., Kumar Chauhan, V., Zhu, T., Clifton, D.A.:
Improving Diagnostics with Deep Forest Applied to Electronic Health Records
Sensors, 2023, 23 (14), 6571
[200] Li, Z., ..., Clifton, D.A., …, H Yang.:
SMKD: Selective Mutual Knowledge Distillation
2023 International Joint Conference on Neural Networks (IJCNN), 2023, 1-8
[199] Liu, X., …, Clifton, D.A., Clifton, L.:
Combining Machine Learning with Cox Models to Identify Predictors for Incident Post-menopausal Breast Cancer in the UK Biobank
Scientific Reports, 2023, 13 (1), 9221
[198] Truong, N.T., ..., Clifton, D.A., …, Thwaites C.L.:
Evaluation of Awake Prone Positioning Effectiveness in Moderate to Severe COVID-19
Wellcome Open Research, 2023 8, 235
[197] Lu, L., Zhu, T., Morelli, D., Creagh, A., ..., Yang, J., Liu, F., …, Clifton, D.A.:
Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review
IEEE Reviews in Biomedical Engineering, 2023
[196] Xu, P., …, Clifton, D.A.:
Multimodal Learning with Transformers
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
[195] Sonnenkalb, L., ..., Clifton, D.A., Lachapelle, A.S., …, Zhu, B.:
Bedaquiline and Clofazimine Resistance in Mycobacterium Tuberculosis: An In-Vitro and In-silico Data Analysis
The Lancet Microbe, 2023, 4 (5), e358-e368
[194] Bobrovitz, N., …, Clifton, D.A., ..., Arora, R.K.:
SeroTracker‐RoB: A decision Rule‐Based Algorithm for Reproducible Risk of Bias Assessment of Seroprevalence Studies Research
Synthesis Methods, 2023, 14 (3), 414-426
[193] Chauhan, V.K., Molaei, S., Tania, M.H., Thakur, A., Zhu, T., Clifton, D.A.:
Adversarial De-confounding in Individualised Treatment Effects Estimation
International Conference on Artificial Intelligence and Statistics, 2023, 837-849
[192] Yang, J., Soltan, A.A.S., …, Clifton, D.A.:
An Adversarial Training Framework for Mitigating Algorithmic Biases in Clinical Machine Learning
NPJ Digital Medicine, 2023, 6 (1), 55
[191] Lu, H., Clifton, D.A., Lu, P., …, MacKillop., L.:
A Deep Learning Approach of Blood Glucose Predictive Monitoring for Women with Gestational
Diabetes 2023
[190] Rohanian, O., …, Clifton, D.A.:
On the Effectiveness of Compact Biomedical Transformers
Bioinformatics, 2023, 39 (3), btad103
[189] Fields, K.G., …, Clifton, D.A., …, Muehlschlegel, J.D.:
Multivariable Prediction Models for Atrial Fibrillation after Cardiac Surgery: A Systematic Review Protocol
BMJ open, 2023, 13 (3), e067260
[188] Niknam, G., Molaei, S., …, Clifton, D.A., Pan, S.:
Graph representation learning based on deep generative gaussian mixture models
Neurocomputing, 2023, 523, 157-169
[187] Chun, M., …, Zhu, T., Clifton, D.A., ..., Clarke, R.:
Heterogeneity in the Diagnosis and Prognosis of Ischemic Stroke Subtypes: 9-year Follow-up of 22,000 Cases in Chinese adults
International Journal of Stroke, 2023, 17474930231162265
[186] Xin, Q.Y., …, Lu, H., Clifton, D.A., …, Luo, M.Y.:
A Distribution-based Selective Optimization Method for Eliminating Periodic Defects in Harmonic Signals
Mechanical Systems and Signal Processing, 2023, 185, 109781
[185] Liu, Z., …, Mertes, G., …, Clifton, D.A.:
Patient Clustering for Vital Organ Failure Using ICD Code with Graph Attention
IEEE Transactions on Biomedical Engineering, 2023
[184] Lu, H.Y., ..., Clifton, D.A.:
Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes
IEEE Reviews in Biomedical Engineering, 2023
[183] Thakur, A., Armstrong, J., Youssef, A., ..., Clifton, D.A.:
Self-aware sgd: Reliable Incremental Adaptation Framework for Clinical AI Models
IEEE Journal of Biomedical and Health Informatics, 2023 27 (3), 1624-1634
[182] Ding, X., ..., Clifton, D.A., Zhu. T.:
Physical Activity and Asthma Symptom Control in Children during COVID-19 Lockdown: A Feasibility Study
Digital Health 9, 2023, 20552076231152165
[181] Vihta, K.D., ..., Clifton, D.A., ..., Walker, A.S.:
Omicron-associated Changes in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Symptoms in the United Kingdom
Clinical Infectious Diseases, 2023, 76 (3), e133-e141
[180] Chung, S.C., ..., Thakur, A., Clifton, D.A., Rui Providencia.:
Prognostic Model for Atrial Fibrillation after Cardiac Surgery: a UK Cohort Study
Clinical Research in Cardiology 2023, 112 (2), 227-235
[179] Rohanian, O., ..., Clifton, D.A., ISARIC Clinical Characterisation Group.:
Lightweight Transformers for Clinical Natural Language Processing
Natural Language Engineering, 2023, pp 1-28
[178] L Taylor, L., ..., Clifton, D.A., Lu, H.:
Wearable Vital Signs Monitoring for Patients with Asthma: A Review
IEEE Sensors Journal 2022
[177] Donnici, C, ..., Clifton, D.A., ..., Arora, R.K.:
Timeliness of Reporting of SARS-Cov-2 Seroprevalence Results and their Utility for Infectious Disease Surveillance
Epidemics 2022, 41, 100645
[176] Ma, X., ..., Clifton, D.A., ..., Arora, R.K.:
Serology Assays used in SARS-Cov-2 Seroprevalence Surveys Worldwide: A Systematic Review and Meta-Analysis of Assay Features, Testing Algorithms, and Performance
Vaccines 2022, 10 (12), 2000
[175] Bergeri, I., ..., Clifton, D.A., ..., Unity Studies Collaborator Group.:
Global SARS-CoV-2 Seroprevalence from January 2020 to April 2022: A Systematic Review and Meta-Analysis of Standardized Population-Based Studies
PLoS Medicine 2022, 19 (11), e1004107
[174] Lu, P., Wang, C.,Hagenah, J., Ghiasi, S., Zhu, T., Thwaites, L., Clifton, D.A.:
Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network
IEEE Transactions on Biomedical Engineering 2022, 70 (4), 1340-1350
[173] Kiyasseh, D., Zhu, T., Clifton, D.A.:
PCPs: Patient Cardiac Prototypes to Probe AI-based Medical Diagnoses, Distill Datasets, and Retrieve Patients
Transactions on Machine Learning Research, 2022
[172] V Kumar, V., Molaei, S., Tania, M.H., Thakur, A., Zhu, T., Clifton, D.A.:
Adversarial De-confounding in Individualised Treatment Effects Estimation
PMLR 2022
[171] Thakur, A., ..., Sharma, P., Zhu, T., Clifton, D.A.:
Incremental Trainable Parameter Selection in Deep Neural Networks
IEEE 2022 [PDF]
[170] Fowler, P.W., The CRyPTIC Consortium (Clifton, D.A).:
Epidemiological Cut-off Values for a 96-well Broth Microdilution Plate for Hgh-throughput Research Antibiotic Susceptibility Testing of M. Tuberculosis
Eur Respir J 2022, 60, 2200239 [PDF]
[169] Tania, M.H., ..., Clifton, D.A.:
Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work
JMIR Form Res 2022, pp e30113 [PDF]
[168] Lu, L., Zhu, T., ..., Clifton, D.A.:
Spectrum Estimation of Heart Rate Variability Using Low-rank Matrix Completion
IEEE EMBS 2022 [PDF]
[167] Chauhan, V.K., Thakur, A., O'Donoghue, O., Clifton, D.A.:
COPER: Continuous Patient State Perceiver
IEEE EMBS 2022 [PDF]
[166] Armstrong, J., Clifton, D.A.:
Continual Learning of Longitudinal Health Records
IEEE EMBS 2022 [PDF]
[165] Lu, P., Ghiasi, S., Hagenah, J., ..., VITAL Consortium, ..., Clifton, D.A., Zhu, T.:
Classification of Tetanus Severity in Intensive-care Settings for Low-income Countries using Wearable Sensing
Sensors 2022, 22, 6554 [PDF]
[164] Brankin, A., The CRyPTIC Consortium (Clifton, D.A.).:
A data Compendium Associating the Genomes of 12,289 Mycobacterium Tuberculosis Isolates with Quantitative Resistance Phenotypes to 13 Antibiotics
PLoS Biology 2022, 20(8), e3001721 [PDF]
[163] Wilson, D., The CRyPTIC Consortium (Clifton, D.A.).:
Genome-wide Association Studies of Global Mycobacterium Tuberculosis Resistance to 13 Antimicrobials in 10,228 Genomes Identify New Resistance Mechanisms
PLoS Biology 2022, 20(8), e3001755 [PDF]
[162] Chun, M., ..., Zhu, T., Clifton, D.A., ..., Cairns, B.J.:
Development, Validation and Comparison of Multivariable Risk Scores for Prediction of Total Stroke and Stroke Types in Chinese Adults: A Prospective Study of 0.5 Million Adults
Stroke & Vascular Neurology 2022, 7, e001251 [PDF]
[161] Lewis, H.C., ..., Clifton, D.A., ..., Bergeri, I.:
SARS-CoV-2 Infection in Africa: A Systematic Review and Meta-analysis of Standardised Seroprevalence Studies, from January 2020 to December 2021
BMJ Global Health 2022, 7, e008793 [PDF]
[160] Yang, J., Clifton, D.A., ..., Lu, H.:
Machine Learning-Based Risk Stratification for Gestational Diabetes Management
Sensors 2022, 22, 4805 [PDF]
[159] Hill, N.R., ..., Clifton, D.A., ..., Cohen, A.T.:
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
ESC, 2022, 3, pp 195-204 [PDF]
[158] Hung, T.M., ..., Clifton, D.A., Ghiasi, S., Greeff, H., Hagenah, J., Lu, P., Schultz, M.:
Direct Medical Costs of Tetanus, Dengue, and Sepsis Patients in an Intensive Care Unit in Vietnam
Front. Public Health 2022, 10:893200 [PDF]
[157] Yang, J., Soltan, A.A.S., Clifton, D.A.:
Machine Learning Generalizability across Healthcare Settings: Insights from Multi-site COVID-19 Screening
npj Digital Medicine 2022, 69 [PDF]
[156] Ghiasi, S., Zhu, T., Lu, P., Hagenah, J., ..., Clifton, D.A.:
Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries
Sensors 2022, 22, 3866 [PDF]
[155] Fowler, P.W., ..., Zhu, T., ..., Clifton, D.A., ..., CRyPTIC Consortium:
A Crowd of BashTheBug Volunteers Reproducibly and Accurately Measure the Minimum Inhibitory Concentrations of 13 Antitubercular Drugs from Photographs of 96-well Broth Microdilution Plates
eLife 2022, 11, e75046 [PDF]
[154] Lu, L., ..., Clifton, D.A.:
Weak Monotonicity with Trend Analysis for unsupervised Feature Evaluation
IEEE Transactions on Cybernetics, 2022 [PDF]
[153] Kiyasseh, D., Zhu, T.T., and Clifton, D.A.:
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals
International Conference of Machine Learning (ICML), 2022
[In press]
[152] Vasey, B., ..., Clifton, D.A., ..., Watkinson, P., Weber, W., Wheatstone, P., McCulloch, P., and the DECIDE-AI Expert Group:
Reporting Guideline for the Early-Stage Clinical Evaluation of Decision Support Systems Driven by Artificial Intelligence: DECIDE-AI
Nature Medicine 28, 2022, pp. 924-933 [PDF]
[151] Soltan, A.A.S., Yang, J., Pattanshetty, R., Novak, A., Yang, Y., Rohanian, O., Beer, S., Soltan, M.A., Thickett, D.R., Fairhead, R., Zhu, T.T., Eyre, D.W., and Clifton, D.A.:
Real-World Evaluation of Rapid and Laboratory-Free COVID-19 Triage for Emergency Care: External Validation and Pilot Deployment of Artificial Intelligence Driven Screening
Lancet Digital Health 4(4), 2022, pp. e266-e278 [PDF]
[150] Sharma, P., Shamout, F.E., Abrol, V., and Clifton, D.A.:
Data Pre-processing using Neural Processes for Modelling Personalised Vital-Sign Time-Series Data
IEEE Journal of Biomedical and Health Informatics 26(4), 2022, pp. 1528-1537 [PDF]
[149] Thakur, A., Sharma, P., and Clifton, D.A.:
Dynamic Neural Graphs Based Federated Reptile for Semi-supervised Multi-Tasking in Healthcare Applications
IEEE Journal of Biomedical and Health Informatics 26(4), 2022, pp. 1761-1772 [PDF]
[148] Vasey, B., ..., Clifton, D.A., ..., Watkinson, P., Weber, W., Wheatstone, P., McCulloch, P., and the DECIDE-AI Expert Group:
Reporting Guideline for the Early-Stage Clinical Evaluation of Decision Support Systems Driven by Artificial Intelligence: DECIDE-AI
BMJ 377, 2022, pp. e070944 [PDF]
[147] Walker, T.M., P. Miotto, ..., and the CRyPTIC Consortium (Kouchaki, S., Yang, Y., Lachapelle, A., and Clifton, D.A.):
The 2021 WHO Catalogue of Mycobacterium Tuberculosis Complex Mutations Associated with Drug Resistance: A Genotypic Analysis
Lancet Microbe, 2022 [PDF]
[146] CRyPTIC Consortium (Kouchaki, S., Yang, Y., Lachapelle, A., and Clifton, D.A.):
Epidemiological Cutoff Values for a 96-Well Broth Microdilution Plate for High-Throughput Research Antibiotic Susceptibility Testing of M. tuberculosis
European Respiratory Journal [In press]
[145] Manley, G.F., Mather, T.A., Pyle, D.M., Clifton, D.A., Rodgers, M., Thompson, G., and Londono, J.M.:
A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events
Frontiers in Earth Science 10, 2022, pp. 807926 [PDF]
[144] Ghiasi, S., Zhu, T.T., Lu, P., Hagenah, J., Khanh, P.N.Q., Hao, N.V., VITAL Consortium, Thwaites, L., and Clifton, D.A.:
Sepsis Mortality Prediction using Wearable Monitoring in Low-Middle Income Countries
Sensors 22(10), 2022, pp. 3866 [PDF]
[143] Lu, H., Hirst, J., Yang, J., MacKillop, L., Clifton, D.:
Standardising the Assessment of Caesarean Birth using an Oxford Caesarean Prediction Score for Mothers with Gestational Diabetes
Healthcare Technology Letters 9(1-2), 2022, pp. 1-8 [PDF]
[142] Ceritli, T., Creagh, A.P., Clifton, D.A.:
Mixture of Input-output Hidden Markov Models for Heterogeneous Disease Progression Modeling
Workshop on Healthcare AI and COVID-19, 2022
[141] Parsons, R.E., Colopy, G.W., Clifton, D.A., and Clifton, L.:
Clinical Prediction Models in Epidemiological Studies: Lessons from the Application of QRISK3 to UK Biobank Data
Journal of Data Science 20(1), 2022, pp. 1-13 [PDF]
[140] Rossi, A., Caloguiri, G., Maffi, S., Pedreschi, D., Clifton, D.A., and Morelli, D.:
Physiological Recovery Among Workers in Long-Distance Sleddog Race: A Case Study on Female Veterinarians in Finnmarkslopet
Work 71(3), 2022, pp. 749-760 [PDF]
[139] Mertes, G., Long, Y., Liu, Z.D.H., Li, Y., Yang, Y., and Clifton, D.A.:
A Deep Learning Approach for the Assessment of Signal Quality of Non-invasive Foetal Electrocardiography
Sensors 22, 2022, pp. 3303 [PDF]
[138] Kiyasseh, D., Zhu, T.T., and Clifton, D.A.:
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age
Neural Information Processing Systems (NeurIPS), 2021 [PDF]
[137] Nigam, G., Thakur, A., ..., Clifton, D.A.:
National GI bleed steering group.: HTH-5 Could Machine Learning (ML) Improve Indices for Predicting Outcome of AUGIB?
Gut, 2021, 70 (Suppl 4), A40-A40
[136] Youssef, A., …, Armstrong, J., …, Taylor, T., ..., Vasey, B., Soltan, A.A.S, Zhu, T, Clifton, D.A., Eyre, D.W.:
Development and Validation of Early Warning Score Systems for COVID‐19 Patients
Healthcare Technology Letters, 2021, 8 (5), 105-117
[135] Zhu, T., Javed, H., Clifton, D.A.:
Comparison of Parametric and Non‐Parametric Bayesian Inference for Fusing Sensory Estimates in Physiological Time‐Series Analysis
Healthcare Technology Letters, 2021, 8 (2), 25-30
[134] Charlton, P.H., Bonnici, T., Tarassenko, L., Clifton, D.A., ..., Alastruey, J.:
An Impedance Pneumography Signal Quality Index: Design, Assessment and Application to Respiratory Rate Monitoring
Biomedical signal processing and control, 2021, 65, PP 102339
[133] DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence
Nature Medicine, 2021, 27 (2), 186-187
[132] Bedford, J.P.N., …, Clifton, D.A, ..., Collins, G.:
Statistical Analysis Plan: Development and Validation of Prediction Models for New-onset Atrial Fibrillation in Patients Admitted to an Intensive Care Unit
University of Oxford, 2021
[131] Kiyasseh, D., Zhu, T.T., and Clifton, D.A.:
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
International Conference on Machine Learning (ICML), PMLR 139, 2021, pp. 5606-5615 [PDF]
[130] Wang, X., Hua, Y., Kodirov, E., Clifton, D.A., and Robertson, N.:
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks
Computer Vision and Pattern Recognition (CVPR), 2021, pp. 752-761 [PDF]
[129] Kiyasseh, D., Zhu, T.T., and Clifton, D.A.:
A Clinical Deep Learning Framework for Continually Learning from Cardiac Signals across Diseases, Time, Modalities, and Institutions
Nature Communications 12, 2021, pp. 4221 [PDF]
[128] Soltan, A.A.S., Kouchaki, S., Zhu, T.T., Kiyasseh, D., Taylor, T., Hussain, Z.B., Peto, T., Brent, A.J., Eyre, D.W., and Clifton, D.A.:
Rapid Triage for COVID-19 using Routine Clinical Data for Patients Attending Hospital: Development and Prospective Validation of an Artificial Intelligence Screening Test
Lancet Digital Health 3(2), 2021, pp. E78-E87 [PDF]
[127] Vasey, B., Clifton, D.A., Collins, G.S., Denniston, A.K., Faes, L., Geerts, B.F., Liu, X., Morgan, L., Watkinson, P.J., and McCulloch, P.:
DECIDE-AI: New Reporting Guidelines to Bridge the Development to Implementation Gap in Clinical Artificial Intelligence
Nature Medicine 27, 2021, pp.186-187 [PDF]
[126] Pimentel, M.A.F., Redfern, O.C., Maylcha, J., Meredith, P., Prytherch, D., Briggs, J., Young, J.D., Clifton, D.A., Tarassenko, L., and Watkinson, P.J.:
Detecting Deteriorating Patients in Hospital: Development and Validation of a Novel Scoring System
American Journal of Respiratory and Critical Care Medicine 204(1), 2021, pp. 44-52 [PDF]
[125] El-Bouri, R., Eyre, D., Watkinson, P., Zhu, T.T., Clifton, D.A.:
Hospital Admission Location Prediction via Deep Interpretable Networks for the Year-Round Improvement of Emergency Patient Care
IEEE Journal of Biomedical and Health Informatics 25(1), 2021, pp. 289-300 [PDF]
[124] Yang, Y., Walker, T., Kouchaki, S., Wang, C.Y., Peto, T.E.A., Crook, D., CRyPTIC Consortium, and Clifton, D.A.:
An End-to-End Heterogeneous Graph Attention Network for Mycobacterium Tuberculosis Drug-Resistance Prediction
Briefings in Bioinformatics 22(6), 2021, pp. 1-13 [PDF]
[123] Bishop, J.A., Javed, H.A., El-Bouri, R., Zhu, T.T., Taylor, T., Peto, T., Watkinson, P., Eyre, D.W., and Clifton, D.A.:
Improving Patient Flow During Infectious Disease Outbreaks using Machine Learning for Real-Time Prediction of Patient Readiness for Discharge
PLoS One 16(11), 2021, pp. e0260476 [PDF]
[122] Faria, S., Carpinteiro, C., Pinto, V., Rodrigues, S.M., Alves, J., Marques, F., Lourenco, M., Santos, P.H., Ramos, A., Cardoso, M.J., Guimaraes, J.T., Rocha, S., Sampaio, P., Clifton, D.A., Mumtaz, M., and Paiva, J.S.:
Forecasting COVID-19 Severity by Intelligent Optical Fingerprinting of Blood Samples
Diagnostics 11(8), 2021, pp. 1309 [PDF]
[121] Bobrovitz, N., Arora, R.K., Cao, C., Boucher, E., Liu, M., Donnici, C., Yanes-Lane, M., Whelan, M., Periman-Arrow, S., Chen, J., Rahim, H., Ilincic, N., Segal, M., Duarte, N., Van Wyk, J., Yan, T., Atmaja, A., Rocco, S., Joseph, A., Penny, L., Clifton, D.A., Williamson, T., Yansouni, C.P., Evans, G.T., Chevrier, J., Papenburg, J., and Cheng, M.P.:
Global Seroprevalence of SARS-CoV-2 Antibodies: A Systematic Review and Meta-analysis
PLoS ONE 16(6), 2021, pp. e0252617 [PDF]
[120] Xiang, T., Ji, N., Clifton, D.A., Lu, L., and Zhang, Y.T.:
Interactive Effects of Heart Rate Variability and P-QRS-T on the Power Density Spectra of ECG Signals
IEEE Journal of Biomedical and Health Informatics 25(11), 2021, pp. 4163-4174 [PDF]
[119] Ji, N., Xiang, T. Bonato, P., Lovell, N.H., Ooi, S.Y., Clifton, D.A., Ding, X., Yan, B.P., Mok, V., Fotiadis, D., and Zhang, Y.T.:
Recommendation to Use Wearable-Based mHealth in Closed-Loop Management of Acute Cardiovascular Disease Patients During the COVID-19 Pandemic
IEEE Journal of Biomedical and Health Informatics 25(4), 2021, pp. 903-908 [PDF]
[118] Ding, X., Clifton, D.A., Ji, N., Lovel, N.H., Bonata, P., Chen, W., Yu, X., Xue, Z., Xiang, T., Long., X., Xu, K., Jiang, X., Wang, Q., Yin, B, Feng, G., and Zhang, Y.T.:
Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic
IEEE Reviews of Biomedical Engineering 14, 2021, pp. 48-70 [PDF]
[117] Youssef, A., Kouchaki, S., Shamout, F., Armstrong, J., El-Bouri, R., Taylor, T., Birrenkott, D., Vasey, B., Soltan, A., Zhu, T.T., Eyre, D.W., Clifton, D.A.:
Development and Validation of Early Warning Score Systems for COVID-19 Patients
Healthcare Technology Letters 8(5), 2021, pp. 105-117 [PDF]
[116] Arning, N., Sheppard, S.K., Bayliss, S., Clifton, D.A., and Wilson, D.J.:
Machine Learning to Predict the Source of Campylobacteriosis Using Whole Genome Data
PLoS Genetics 17(10), 2021, pp. e1009436 [PDF]
[115] Jiang, X., Xu, K., Dai, C., Clifton, D.A., Clancy, E.A., Akay, M., and Chen, W.:
Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification
IEEE Journal of Biomedical and Health Informatics 25(4), 2021, pp. 1070-1079 [PDF]
[114] Jiang, X., Xu, K., Dai, C., Clifton, D.A., Clancy, E.A., Akay, M., and Chen, W.:
Neuromuscular Password-Based User Authentication
IEEE Transactions on Industrial Informatics 17(4), 2021, pp. 2641-2652 [PDF]
[113] Manley, G.F., Mather, T.A., Pyle, D.M., Clifton, D.A., Rodgers, M., Thompson, G., and Roman, D.C.:
Machine Learning Approaches to Identifying Changes in Eruptive State Using Multi-Parameter Datasets from the 2006 Eruption of Augustine Volcano, Alaska
JGR Solid Earth 126(12), 2021, pp. e2021JB022323 [PDF]
[112] El-Bouri, R., Taylor, T., Youssef, A., Zhu, T.T., and Clifton, D.A.:
Machine Learning in Patient Flow: A Review
Progress in Biomedical Engineering 3, 2021, pp. 022002 [PDF]
[111] Chun, M., Clarke, R., Cairns, B.J., Clifton, D.A., Bennett, D., Chen, Y., Guo, Y., Pei, P., Lv, J., Yu, C., Yang, L., Li, L., Chen, Z., and Zhu, T.T.:
Stroke Risk Prediction using Machine Learning: A Prospective Cohort Study of 0.5 Million Chinese Adults
Journal of the Americal Medical Informatics Association 28(8), 2021, pp. 1719-1727 [PDF]
[110] Shamout, F., Zhu, T.T., and Clifton, D.A.:
Machine Learning for Clinical Outcome Prediction
IEEE Reviews in Biomedical Engineering 14, 2021, pp. 116-126 [PDF]
[109] Morelli, D., Rossi, A., Bartolini, L., Cairo, M., and Clifton, D.A.:
SDNN24 Estimation from Semi-Continuous HR Measures
Sensors 21, 2021, pp. 1463 [PDF]
[108] Chun, M., Clarke, R., Zhu, T.T., Bennet, D., Clifton, D.A., Chen, Y., Yang, L., Chen, Z., and Cairns, B.:
Utility of Single versus Sequential Measurements of Risk Factors for Prediction of Stroke in Chinese Adults
Scientific Reports 11, 2021, pp. 17575 [PDF]
[107] Gordon, J., Norman, M., Hurst, M., Mason, T., Dickerson, C., Sandler, B., Pollock, K.G., Farooqui, U., Groves, L., Tsang, C., Clifton, D.A., Bakhai, A., and Hill, N.R.
Using Machine Learning to Predict Anticoagulation Control In Atrial Fibrillation: A UK Clinical Practice Research Datalink Study
Informatics in Medicine Unlocked 25, 2021, pp. 100688 [PDF]
[106] Velardo, C., Clifton, D.A., Hamblin, S., Khan, R., Tarassenko, L., and MacKillop, L.:
Towards a Multivariate Prediction Model of Pharmacological Treatment for Women with Gestational Diabetes Mellitus
Journal of Medical Internet Research 23(3), 2021, e21435, pp. 1-14 [PDF]
[105] Chau, N.V.V., Hai, H.B., Greeff, H., Quoc, K.P.N., Trieu, H.T., Khoa, L.D.V., Nguyen, C.N., Van, H.M.T., Yen, L.M., Tan, L.V., Dung, N.T., Clifton, D.A., Yacoub, S., and Thwaites, C.L.:
Wearable Remote Monitoring for Patients with COVID-19 in Low-Resource Settings: Case Study
BMJ Innovations 7, 2021, s12-15 [PDF]
[104] Van, H.M.T., Hao, N.V., Quoc, K.P.N., Hai, H.B., Khoa, L.D.V., Yen, L.M., Nhat, P.T.H., Duong, H.T.H., Thuy, D.B., Zhu, T.T, Greeff, H., Clifton, D.A., and Thwaites, C.L.:
Vital Sign Monitoring using Wearable Devices in a Vietnamese Intensive Care Unit
BMJ Innovations 7, 2021, s1-5 [PDF]
[103] Whelan, M., Biggs, C., Areia, C., King, E., Lawson, B., Newhouse, N., Ding, X.R, Velardo, C., Bafadhel, M., Tarassenko, L., Watkinson, P., Clifton, D.A., and Farmer, A.:
Recruiting Patients to a Digital Self-Management Study whilst in Hospital for a Chronic Obstructive Pulmonary Disease Exacerbation: A Feasibility Analysis
Digital Health 7, 2021, pp. 1-5 [PDF]
[102] El-Bouri, R., Eyre, D., Watkinson, P., Zhu, T.T, and Clifton, D.A.:
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location
International Conference on Machine Learning (ICML), PMLR 119, 2020, pp. 2848-2857 [PDF]
[101] Zhu, T.T., Watkinson, P., and Clifton, D.A.:
Smartwatch Data Help Detect COVID-19
Nature Biomedical Engineering 4, 2020, pp. 1125-1127 [PDF]
(Invited article)
[100] Manley, G.F., Pyle, D.M., Mather, T.A., Rodgers, M., Clifton, D.A., Stokell, B.G., Thompson, G., Londono, J.M., and Roman, D.C.:
Understanding the Timing of Eruption End using a Machine Learning Approach to Classification of Seismic Time Series
Journal of Volcanology and Geothermal Research 401, 2020, pp. 106917 [PDF]
[99] Shamout, F.E., Zhu, T.T., Sharma, P., Watkinson, P.J., and Clifton, D.A.:
Deep Interpretable Early Warning System for the Detection of Clinical Deterioration
IEEE Journal of Biomedical and Health Informatics 24(2), 2020, pp. 437-446 [PDF]
[98] Kiyasseh, D., Abebe Tadesse, G., Nhan, L.N.T., Tan, L.V., Thwaites, L., Zhu, T.T., and Clifton, D.A.:
PlethAugment: GAN-Based PPG Augmentation for Medical Diagnosis in Low-Resource Settings
IEEE Journal of Biomedical and Health Informatics 24(11), 2020, pp. 3226-3235 [PDF]
[97] Zhu, T.T., Javed, H., and Clifton, D.A.:
Comparison of Parametric and Non-parametric Bayesian Inference for Fusing Sensory Estimates in Physiological Time-series Analysis
Healthcare Technology Letters [In press][PDF]
[96] Manandhar, A., Greeff, H., Thomson, P., Hope, R., and Clifton, D.:
Shallow Aquifer Monitoring Using Handpump Vibration Data
Journal of Hydrology X 8, 2020, pp. 100057 [PDF]
[95] Manandhar, A., Fischer, A., Bradley, D.J., Salehin, M., Sirajul Islam, M., Hope, R., and Clifton, D.A.:
Machine Learning to Evaluate Impacts of Flood Protection in Bangladesh, 1983-2014
Water 12(2), 2020, pp. 483 [PDF]
[94] Hill, N.R., Arden, C., Beresford-Hulme, L., Camm, A.J., Clifton, D.A., Davies, D.W., Farooqui, U., Gordon, J., Groves, L., Hurst, M., Lawton, S., Lister, S., Mallen, C., Martin, A.C., McEwan, P., Pollock, K.G., Rogers, J., Sandler, B., Sugrue, D.M., Cohen, A.T.: Identification of Undiagnosed Atrial Fibrillation Patients Using a Machine Learning Risk Prediction Algorithm and Diagnostic Testing (PULsE-AI): Study Protocol for a Randomised Controlled Trial
Contemporary Clinical Trials 99, 2020, pp. 106191 [PDF]
[93] Gordon, J., Norman, M., Hurst, M., Mason, T., Dickerson, C., Sandler, B., Pollock, K.G., Farooqui, U., Clifton, D., Groves, L., Tsang, C., Bakhai, A., and Hill, N.R.
PCV93 Using Machine Learning to Predict Anticoagulation Control In Atrial Fibrillation: A UK Retrospective Database Study
Value in Health 23(2), 2020, pp. S503 [PDF]
[92] Wilson, D.J. and the CRyPTIC Consortium (Kouchaki, S., Yang, Y., Lachapelle, A., and Clifton, D.A.):
GenomegaMap: Within-Species Genome-Wide dN/dS Estimation from over 10,000 Genomes
Molecular Biology and Evolution 37(8), 2020, pp. 2450-2460 [PDF]
[91] Rossi, A., Pedreschi, D., Clifton, D.A., and Morelli, D.:
Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artefacts
Sensors, 2020 20(24), pp. 7122 [PDF]
[90] Charlton, P.H., Bonnici, T., Tarassenko, L., Clifton, D.A., Beale, R., Watkinson, P.J., and Alastruey, J.:
An Impedance Pneumography Signal Quality Index: Design, Assessment and Application to Respiratory Rate Monitoring
Biomedical Signal Processing and Control 65, 2020, pp. 102339 [PDF]
[89] Wong, D., Gerry, S., Shamout, F., Clifton, D.A., Pimentel, M.A.F., and Watkinson, P.:
Cross-Sectional Centiles of Blood Pressure by Age and Sex: A Four Hospital Database Retrospective Observational Analysis
BMJ Open 10, 2020, pp. e033618 [PDF]
[88] Kouchaki, S., Yang, Y., Walker, T.M., Walker, A.S., CRyPTIC Consortium, Peto, T.E.A., Crook, D.W., and Clifton, D.A.:
Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking
Frontiers in Microbiology 11, 2020, pp. 667 [PDF]
[87] Chen, W., Clifton, D.A., and Telfer, B.:
Guest Editorial: Integrative Sensor Networks, Informatics, and Modeling for Precision and Preventative Medicine
IEEE Journal of Biomedical and Health Informatics 24(7), 2020, pp. 1858-1859 [PDF]
[86] Abebe Tadesse, G., Javed, H., Thanh, N.L.N., Ha, T.H.D., Le, V.T., Thwaites, L., Clifton, D.A., and Zhu, T.T.:
Multi-modal Diagnosis of Infectious Diseases in the Developing World
IEEE Journal of Biomedical and Health Informatics 24(7), 2020, pp. 2131-2141 [PDF]
[85] Duong, H.T.H., Abebe Tadesse, G., Nhat, P.T.H., Hao, N.V., Prince, J., Duong, T.D., Kien, T.T., Nhat, L.T.H., Van, T.L., Pugh, C., Loan, H.T., Van, V.C.N., Minh, Y.L., Zhu, T.T., Clifton, D.A., and Thwaites, L.:
Heart Rate Variability as an Indicator of Autonomic Nervous System Disturbance in Tetanus
American Journal of Tropical Medicine and Hygiene 102(5), 2020, pp. 403-407 [PDF]
[84] Abebe Tadesse, G., Zhu, T.T., Nhan, L.N.T., Nguyen, T.H., Ha, T.H.D., Truong, H.K., Pham, V.Q., Duc, D.T., Lam, M.Y., Van Doorn, H.R., Nguyen, V.H., Prince, J., Javed, H., Kiyasseh, D., Le, V.T., Thwaites, L., and Clifton, D.A.:
Severity Detection Tool for Patients with Infectious Disease
Healthcare Technology Letters 7(2), 2020, pp. 45-50 [PDF]
[83] Rossi, A., Da Pozzo, E., Menicagli, D., Tremolanti, C., Priami, C., Sirbu, A., Clifton, D.A., Martini, C., and Morelli, D.:
A Public Dataset of 24-h Multi-levels Psycho-Physiological Responses in Young Healthy Adults
Data 5(4), 2020, pp. 91 [PDF]
[82] Leeming, G., Ainsworth, J., and Clifton, D.A.:
Blockchain in Health Care: Hype, Trust, and Digital Health
The Lancet 393, 2019, pp. 2476-2477 [PDF]
[81] Yang, Y., Walker, T.M., Walker, A.S., Wilson, D.J., Peto, T.E.A., Crook, D.W., Shamout, F., CRyPTIC Consortium, Zhu, T.T., and Clifton, D.A.:
DeepAMR for Predicting Co-occurrent Resistance of Mycobacterium Tuberculosis
Bioinformatics 35(18), 2019, pp. 3240-3249 [PDF]
[80] Zhu, T.T., Pimentel, M.A.F.,Clifford, G.D., and Clifton, D.A.:
Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalising Care
IEEE Journal of Biomedical and Health Informatics 23(1), 2019, pp. 47-58 [PDF]
[79] Colopy, G.W., Roberts, S.J., and Clifton, D.A.:
Gaussian Processes for Personalized Interpretable Volatilty Metrics in the Step-Down Ward
IEEE Journal of Biomedical and Health Informatics 23(3), 2019, pp. 949-959 [PDF]
[78] Zhu, T.T., Colopy, G.W., Yang, Y., Pugh, C.W., and Clifton, D.A.:
Patient-Specific Physiological Monitoring and Prediction Using Structured Gaussian Processes
IEEE Access 7(1), 2019, pp. 58094-58103 [PDF]
[77] Clifton, L., Birks, J., and Clifton, D.A.:
The Correlation Between Baseline Score and Post-intervention Score, and its Implications on Statistical Analysis
Trials 20(1), 2019, pp. 1-6 [PDF]
[76] Clifton, L., Birks, J., and Clifton, D.A.:
Comparing Different Ways of Calculating Sample Size for Two Independent Means: A Worked Example
Contemporary Clinical Trials Communications 13, 2019, pp. 100309 [PDF]
[75] Malycha, J., Farajidavar, N., Pimentel, M.A.F., Redfern, O., Clifton, D.A., Tarassenko, L., Meredith, P., Prytherch, D., Ludbrook, G., Young, D., and Watkinson, P.J.:
The Effect of Fractional Inspired Oxygen Concentration on Early Warning Score Performance: A Database Analysis
Resuscitation 139, 2019, pp. 192-199 [PDF]
[74] Kouchaki, S., Yang, Y., Walker, T.M., Walker, S., Wilson, D.J., Peto, T.E.A., Crook, D.W., and Clifton, D.A.:
Application of Machine Learning Techniques to Tuberculosis Drug Resistance Analysis
Bioinformatics 35(13), 2019, pp. 2276-2282 [PDF]
[73] Shamout, F., Zhu, T.T., Clifton, L., Briggs, J., Prytherch, D., Meredith, P., Tarassenko, L., Watkinson, P.J., and Clifton, D.A.:
Early Warning Score Adjusted for Age to Predict the Composite Outcome of Mortality, Cardiac Arrest or Unplanned Intensive Care Unity Admission Using Observational Vital-Sign Data: A Multicentre Development and Validation
BMJ Open 9, 2019, pp. e033301 [PDF]
[72] Sharma, P., Manandhar, A., Thomson, P., Katuva, J., Hope, R., and Clifton, D.A.:
Combining Multi-modal Statistics for Welfare Prediction Using Deep Learning
Sustainability 11(22), 2019, pp. 6312 [PDF]
[71] Malycha, J., Bonnici, T., Clifton, D.A., Ludbrook, G., Young, D., and Watkinson, P.J.:
Patient Centred Variables with Univariate Associations with Unplanned ICU Admission: A Systematic Review
BMC Medical Informatics and Decision Making 19(98), 2019, pp. 1-9 [PDF]
[70] Morelli, D., Bartolini, L, Rossi, A., and Clifton, D.A.:
A Computationally Efficient Algorithm to Obtain an Accurate and Interpretable Model of the Effect of Circadian Rhythm on Resting Heart Rate
Physiological Measurement 40(9), 2019, pp. 095001 [PDF]
[69] Morelli, D., Rossi, A., Cairo, M., and Clifton D.A.:
Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations
Sensors 19(14), 2019, pp. 3163 [PDF]
[68] Turner, H.C, Nguyen, V.H., Yacoub, S., Van, H.M.T., V.H., Clifton, D.A., Thwaites, G.E., Dondorp, A.M., Thwaites, C.L., and Nguyen, V.V.C.:
Achieving Affordable Critical Care in Low and Middle-Income Countries
BMJ Global Health 4(3), 2019, pp. 1-4 [PDF]
[67] Jarchi, D., Charlton, P., Pimentel, M.A.F., Casson, A.J., Tarassenko, L., and Clifton, D.A.:
Estimation of Respiratory Rate from Motion-Contaminated Photoplethysmography Signals Incorporating Accelerometry
Healthcare Technology Letters 6(1), 2019, pp.19-26 [PDF]
[66] Hill, N.R., Ayoubkhani, D., McEwan, P., Sugrue, D.M., Farooqui, U., Lister, S., Lumley, M., Bakhai, A., Cohen, A.T., O'Neill, M., Clifton, D.A., and Gordon, J.:
Predicting Atrial Fibrillation in Primary Care using Machine Learning
PLoS One 14(11), 2019, pp. e0224582 [PDF]
[65] Allix-Beguec, C., ..., Clifton, D.A., Yang, Y., ..., Zhu, B.:
Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing
New England Journal of Medicine 379(15), 2018, pp. 1403-1415 [PDF]
[64] Colopy, G.W., Roberts, S.J., and Clifton, D.A.:
Bayesian Optimisation of Personalised Models for Patient Vital-Sign Monitoring
IEEE Journal of Biomedical and Health Informatics 22(2), 2018, pp. 301-310 [PDF]
(Invited article)
[63] Luca, S., Pimentel, M.A.F., Watkinson, P.J., and Clifton, D.A.:
Point Process Models for Novelty Detection on Spatial Point Patterns and their Extremes
Computational Statistics and Data Analysis 125, 2018, pp. 86-103 [PDF]
[62] Yang, Y., Niehaus, K.E., Walker, T.M., Iqbal, Z., Walker, A.S., Wilson, D.J., Peto, T.E.A., Crook, D.W., and Clifton, D.A.:
Machine Learning for Classifying Tuberculosis Drug-Resistance from DNA Sequencing Data
Bioinformatics 34(10), 2018, pp. 1666-1671 [PDF]
[61] Yang, Y., Peng, Z., Dong, X., Zhang, W., and Clifton, D.A.:
Component Isolation for Multi-component Signal Analysis using a Non-parametric Gaussian Latent Feature Model
Mechanical Systems and Signal Processing 103, 2018, pp. 368-380 [PDF]
[60] Zhu, T.T., Yang, Y., Johnson, A.E.W., Clifford, G.D., and Clifton, D.A.:
Bayesian Fusion of Physiological Measurements using a Signal Quality Extension
Physiological Measurement 39(6), 2018, pp. 065008 [PDF]
[59] Birrenkott, D.A., Pimentel, M.A.F., Watkinson, P.J., and Clifton, D.A.:
A Robust Fusion Model for Estimating Respiratory Rate from Photoplethysmography and Electrocardiography
IEEE Transactions on Biomedical Engineering 65(9), 2018, pp. 2033-2041 [PDF]
[58] Greeff, H., Manandhar, A., Thomson, P., Hope, R., and Clifton, D.A.:
Distributed Inference Condition Monitoring System for Rural Infrastructure in the Developing World
IEEE Sensors Journal 19(5), 2018, 1820-1828 [PDF]
[57] Redfern, O.C., Pimentel, M.A.F., Prytherch, D., Meredith, P., Clifton, D.A., Tarassenko, L., Smith, G.B., and Watkinson, P.J.:
Predicting In-Hospital Mortality and Unanticipated Admissions to the Intensive Care Unit using Routinely Collected Blood Tests and Vital Signs: Development and Validation of a Multivariable Model
Resuscitation 133, 2018, pp. 75-81 [PDF]
[56] Watkinson, P.J., Pimentel, M.A.F., Clifton, D.A., and Tarassenko, L.:
Manual Centile-Based Early Warning Scores Derived from Statistical Distributions of Observational Vital-Sign Data
Resuscitation 129, 2018, pp. 55-60 [PDF]
[55] Jarchi, D., Salvi, D., Tarassenko, L., and Clifton, D.A.:
Validation of Instantaneous Respiratory Rate using Reflectance PPG from Different Body Positions
Sensors 18(11), 2018, pp. 3705 [PDF]
[54] Lee, R., Jarchi, D., Perera, R., Jones, A., Cassimjee, I., Handa, A., and Clifton, D.A.:
Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans
European Journal of Vascular and Endovascular Surgery Short Reports 39, 2018, pp. 24-28 [PDF]
[53] Jarchi, D., Rodgers, S.J., Tarassenko, L., and Clifton, D.A.:
Accelerometry-Based Estimation of Respiratory Rate for Post-Intensive Care Patient Monitoring
IEEE Sensors Journal 18(12), 2018, pp. 4981-4989 [PDF]
[52] Charlton, P.H., Birrenkott, D.A., Bonnici, T., Pimentel, M.A.F., Johnson, A.E.W., Alastruey, J., Tarassenko, L., Watkinson, P.J., Beale, R., and Clifton D.A.:
Breathing Rate Estimation from the Electrocardiogram and Photoplethysmogram: A Review
IEEE Reviews in Biomedical Engineering 11, 2018, pp. 2-20 [PDF]
(Invited article)
[51] Morelli, D., Bartolini, L., Colombo, M., Plans, D., and Clifton, D.A.:
Profiling the Propagation of Error from PPG to HRV Features in a Wearable Physiological-Monitoring Device
Healthcare Technology Letters 5(2), 2018, pp. 59-64 [PDF]
[50] Tomlinson, H., Pimentel, M.A.F., Gerry, S., Clifton, D.A., Tarassenko, L., and Watkinson, P.J.:
Smoothing Effect in Vital-Sign Recordings: Fact or Fiction? A Retrospective Cohort Analysis of Manual and Continuous Vital-Sign Measurements to Assess Data Smoothing in Post-Operative Care
Anesthesia and Analgesia 127(4), 2018, pp. 960-966 [PDF]
[49] Colchester, F.E., Marais, H.G., Thomson, P., Hope, R., and Clifton, D.A.:
Accidental Infrastructure for Groundwater Monitoring in Africa
Environmental Modelling and Software 91, 2017, pp. 241-250 [PDF]
[48] Pimentel, M.A.F., Johnson, A.E.W., Charlton, P.H., Birrenkott, D.A., Watkinson, P.J., Tarassenko, L., and Clifton, D.A.:
Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters
IEEE Transactions on Biomedical Engineering 64(8), 2017, pp. 1914-1923 [PDF]
[47] Charlton, P.H., Bonnici, T., Tarassenko, L., Alastruey, J., Clifton D.A., Beale, R., and Watkinson, P.J.:
Extraction of Respiratory Signals from the Electrocardiogram and Photoplethysmogram: Technical and Physiological Determinants
Physiological Measurement 38, 2017, pp. 669-690 [PDF]
[46] Pullinger, R., Wilson, S., Way, R., Santos, M., Wong, D., Clifton, D.A., Birks, J., and Tarassenko, L.:
Implementing an Electronic Observation and Early Warning Score Chart in the Emergency Department: A Feasibility Study
European Journal of Emergency Medicine 24(6), 2017, pp. e11-e16 [PDF]
[45] Clifton, D.A.:
Case Studies of Medical Monitoring Systems
Equipment Health Monitoring in Complex Systems, Artech House, 2017, pp. 125-147 [PDF]
[44] King, S., Mills, A., Kadirkamanathan, V., and Clifton, D.A.:
Future Directions in Health Monitoring
Equipment Health Monitoring in Complex Systems, Artech House, 2017, pp. 177-203 [PDF]
[43] Luca, S., Clifton, D.A., and Vanrumste, B.:
One-Class Classification of Point Patterns of Extremes
Journal of Machine Learning Research 17(191), 2016, pp. 1-21 [PDF]
[42] Earle, S.G., Wu, C-H., Charlesworth, J., Stoesser, N., Gordon, N.C., Walker, T.M., Spencer, C.C.A., Iqbal, Z., Clifton, D.A., Smith, E.G., Ismail, N., Llewelyn, M.J., Peto, T.E.A., Crook, D.W., McVean, G., Walker, A.S., and Wilson, D.J.:
Identifying Lineage Effects When Controlling for Population Structure Improves Power in Bacterial Association Studies
Nature Microbiology 1(16041), 2016, DOI: 10.1038/NMICROBIOL.2016.41 [PDF]
[41] Johnson, A.E.W., Ghassemi, M.M., Nemati, S., Niehaus, K.E., Clifton, D.A., and Clifford, G.D.:
Machine Learning and Decision Support in Critical Care
Proceedings of the IEEE 104(2), 2016, pp. 444-466 [PDF]
[40] Wilson, S., Wong, D., Pullinger, R., Way, R., Clifton, D.A., and Tarassenko, L.:
Analysis of a Data Fusion System for Continuous Vital-Sign Monitoring in an Emergency Department
European Journal of Emergency Medicine 23(1), 2016, pp. 28-32 [PDF]
[39] Charlton, P.H., Bonnici, T., Tarassenko, L., Clifton D.A., Beale, R., and Watkinson, P.J.:
An Assessment of Algorithms for Estimation of Respiratory Rate from the Electrocardiogram and Photoplethysmogram
Physiological Measurements 37, 2016, pp. 610-626 [PDF]
(Awarded Institute of Physics Martin Black Prize)
[38] Niehaus, K.E. and Clifton, D.A.:
Machine Learning for Chronic Disease
in "Machine Learning for Healthcare Technologies", ed. Clifton, D.A.
IET Press, 2016, pp. 227-250 [PDF]
[37] Yang, Y., Niehaus, K.E., and Clifton, D.A.:
Predicting Antibiotic Resistance from Genomic Data
in "Machine Learning for Healthcare Technologies", ed. Clifton, D.A.
IET Press, 2016, pp. 203-226 [PDF]
[36] Zhu, T.T., Clifford, G.D., and Clifton, D.A.:
A Bayesian Model for Fusing Biomedical Labels
in "Machine Learning for Healthcare Technologies", ed. Clifton, D.A.
IET Press, 2016, pp. 127-160 [PDF]
[35] Pimentel, M.A.F. and Clifton, D.A.:
Patient Physiological Monitoring with Machine Learning
in "Machine Learning for Healthcare Technologies", ed. Clifton, D.A.
IET Press, 2016, pp. 111-126 [PDF]
[34] Clifton, D.A.:
Machine Learning for Healthcare Technologies: An Introduction
in "Machine Learning for Healthcare Technologies", ed. Clifton, D.A.
IET, 2016, pp. 1-6 [PDF]
[33] Walker, T.M., Kohl, T.A., Omar, S.V., Hedge, J., Del Oio Elias, C., Bradley, P., Iqbal, Z., Feuerriegel, S., Niehaus, K.E., Wilson, D.J., Clifton, D.A., Kapatai G., Ip, C., Bowden, R., Drobniewski, F., Allix-Beguec, C., Gaudin, C., Parkhill, J., Diel, R., Supply, P., Crook, D., Smith, E.G., Walker, A.S., Ismail, N., Nieman, S., and Peto, T.E.A.:
Whole-Genome Sequencing for Prediction of Mycobacterium Tuberculosis Drug-Susceptibility and Resistance: A Retrospective Cohort Study
Lancet Infectious Diseases 15(10), 2015, pp. 1193-1202 [PDF]
[32] Duerichen, R., Pimentel, M.A.F., Clifton, L., Schweikard, A., and Clifton, D.A.:
Multi-task Gaussian Processes for Multivariate Physiological Time-Series Analysis
IEEE Transactions on Biomedical Engineering 62(1), 2015, pp. 314-322 [PDF][Toolbox]
[31] Clifton, D.A., Clifton, L., Sandu, D.M., Smith, G.B., Tarassenko, L., Vollam, S., and Watkinson, P.J.:
"Errors" and Omissions in Paper-Based Early Warning Scores: The Association with Changes in Vital Signs - A Database Analysis.
British Medical Journal Open 5, e007376, 2015, pp. 1-7 [PDF]
[30] Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D.A., Vallance, D., and Tarassenko, L.:
Signal Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring
IEEE Journal of Biomedical and Health Informatics 19(3), 2015, pp. 832-838 [PDF]
[29] Clifton, D.A., Niehaus, K.E., Charlton, P., and Colopy, G.W.:
Health Informatics via Machine Learning for the Clinical Management of Patients
Yearbook of Medical Informatics 10, 2015, pp. 38-43 [PDF]
[28] Clifton, D.A., Pimentel, M.A.F., Niehaus, K., Clifton, L., Peto, T.E.A., Crook, D.W., and Watkinson, P.J.:
Intelligent Electronic Health Systems
in "Telemedicine and Electronic Medicine", eds. Eren, H. and Webster, J.G.,
CRC Press, 2015, pp. 73-97 [PDF]
[27] Pimentel, M.A.F., Charlton, P.H., and Clifton, D.A.:
Probabilistic Estimation of Respiratory Rate from Wearable Sensors
in "Wearable Electronic Sensors", ed. Mukhopadhyay, S., Smart Sensors, Measurement and Instrumentation 15, 2015, pp. 241-262 [PDF]
[26] Zhu, T.T., Dunkley, N., Behar, J., Clifton, D.A., and Clifford, G.D.:
Fusing Continuous-Valued Medical Labels Using a Bayesian Model
Annals of Biomedical Engineering 43(12), 2015, pp. 2892-2902 [PDF]
[25] Clifton, D.A. and Tarassenko, L.:
Novelty Detection in Jet Engine Vibration Spectra
Int. Journal of Condition Monitoring 5(2), 2015, pp.2-7 [PDF]
[24] Clifton, L., Clifton, D.A., Farmery, A.D., and Hahn, C.E.W.:
A Non-invasive Method for Estimating Lung Function
Int. Journal of Condition Monitoring 5(3), 2015, pp.2-5 [PDF]
[23] Clifton, L., Clifton, D.A., Pimentel, M.A.F., Watkinson, P.J., and Tarassenko, L.:
Predictive Monitoring of Mobile Patients by Combining Clinical Observations with Data from Wearable Sensors
IEEE Journal of Biomedical and Health Informatics 18(3), 2014, pp. 722-730 [PDF]
(Featured article for IEEE JBHI.)
[22] Clifton, D.A., Hugueny, S., Clifton, L., and Tarassenko, L.:
Extending the Generalised Pareto Distribution for Novelty Detection in High-Dimensional Spaces
Journal of Signal Processing Systems 74, 2014, pp. 323-339 [PDF]
[21] Pimentel, M.A.F., Clifton, D.A., Clifton, L., and Tarassenko, L.:
A Review of Novelty Detection
Signal Processing 99, 2014, pp. 215-249 [PDF] [Toolbox]
[20] Tarassenko, L., Villarroel, M., Guazzi, A., Jorge, J., Clifton, D.A., and Pugh, C.:
Non-contact Video-Based Vital Sign Monitoring Using Ambient Light and Auto-regressive Models
Physiological Measurement 35, 2014, pp. 807-831 [PDF]
(Awarded Institute of Physics Martin Black Prize)
[19] Clifton, L., Clifton, D.A., Zhang, Y., Watkinson, P.J., Tarassenko, L., and Yin, H:
Probabilistic Novelty Detection with Support Vector Machines
IEEE Transactions on Reliability 63(2), 2014, pp. 455-467 [PDF]
[18] Clifton, D.A., Clifton, L., Hugueny, S., Wong, D., and Tarassenko, L.:
An Extreme Function Theory for Novelty Detection
IEEE Journal of Selected Topics on Signal Processing 7(1), 2013, pp. 28-37 [PDF][CODE]
[17] Clifton, L., Clifton, D.A., Pimentel, M.A.F., Watkinson, P.J., and Tarassenko, L.:
Gaussian Processes for Personalised e-Health Monitoring with Wearable Sensors
IEEE Transactions on Biomedical Engineering 60(1), 2013, pp. 193-197 [PDF]
[16] Clifton, L., Clifton, D., Hahn, C.E.W., and Farmery, A.D.
A Non-invasive Method for Estimating Cardiopulmonary Variables Using Breath-by-Breath Injection of Two Tracer Gases
IEEE Journal of Translational Engineering in Health and Medicine 1, 2013, pp. 1-8 [PDF]
[15] Clifton, L., Clifton, D., Hahn, C.E.W., and Farmery, A.D.:
Assessment of Lung Function Using a Non-invasive Oscillating Gas-Forcing Technique
Respiratory Physiology & Neurobiology, 189(1), 2013, pp. 174-182 [PDF]
[14] Clifton, D.A., Wong, D., Clifton, L., Pullinger, R., and Tarassenko, L.:
A Large-Scale Clinical Validation of an Integrated Monitoring System in the Emergency Department
IEEE Journal of Biomedical & Health Informatics 17(4), 2013, pp. 835-842 [PDF]
(Awarded IMIA Prize, 2015)
[13] Pimentel, M.A.F., Clifton, D.A., Clifton, L., Watkinson, P.J., and Tarassenko, L.:
Modelling Physiological Deterioration in Post-operative Patient Vital-Sign Data
Medical & Biological Engineering & Computing 51, 2013, pp. 869-877 [PDF]
[12] Bonnici, T., Clifton, D.A., Watkinson, P.J., and Tarassenko, L.:
The Digital Patient
Clinical Medicine 13(3), 2013, pp. 252-257 [PDF]
[11] Wilson, S., Wong, D., Clifton, D.A., Fleming, S., Way, R., Pullinger, R., and Tarassenko, L.:
Track and Trigger in an Emergency Department: an Observational Evaluation Study.
Emergency Medicine Journal 30, 2013, pp. 186-191 [PDF]
[10] Khalid, S., Clifton, D.A., Clifton, L.A., and Tarassenko, L.:
A Two-Class Approach to the Detection of Physiological Deterioration in Patient Vital Signs, with Clinical Label Refinement
IEEE Transactions on Information Technology in Biomedicine 16(6), 2012, pp. 1231-1238 [PDF]
[9] Clifford, G.D. and Clifton, D.A.:
Annual Review: Wireless Technology in Disease State Management and Medicine
Annual Review of Medicine 63, 2012, pp. 479-492 [2010 Impact Factor 12.5] [PDF]
[8] Meredith, D., Clifton, D.A., Charlton, P., Brooks, J., Pugh, C.W., and Tarassenko, L.:
Photoplethysmographic Derivation of Respiratory Rate: A Review of Relevant Respiratory and Circulatory Physiology
Journal of Medical Engineering and Technology 36(1), 2012, pp. 60-66 [PDF]
[7] Tarassenko, L. and Clifton, D.A.:
Semiconductor Wireless Technology for Chronic Disease Management
Electronics Letters S30, 2011, pp. 30-32 [PDF]
[6] Tarassenko, L., Clifton, D.A., Pinsky, M.R., Hravnak, M.T., Woods, J.R., and Watkinson, P.:
Centile-Based Early Warning Scores Derived from Statistical Distributions of Vital Signs.
Resuscitation 82 (8), 2011, pp. 1013-1018 [PDF]
[5] Hugueny, S., Clifton, D.A., and Tarassenko, L.:
Probabilistic Patient Monitoring with Multivariate, Multimodal Extreme Value Theory
Communications in Computer Science 127, 2011, pp. 199-211 [PDF]
[4] Clifton, D.A., Hugueny, S., and Tarassenko, L.:
Novelty Detection with Multivariate Extreme Value Statistics
Journal of Signal Processing Systems 65, 2011, pp. 371-389 [PDF][CODE]
[3] King, S., Bannister, P.R., Clifton, D.A., and Tarassenko, L.:
Probabilistic Approaches to Condition Monitoring of Aerospace Engines
IMechE Part G: Journal of Aerospace Engineering 223 (G5), 2009, pp. 533-541 [PDF]
[2] Tarassenko, L., Clifton, D.A., Bannister, P.R., King, S., and King, D.:
Novelty Detection
Encyclopaedia of Structural Health Monitoring 2, Wiley, 2009, pp. 653-675 [PDF]
[1] Clifton, D.A., Clifton, L., Bannister, P.R., and Tarassenko, L.:
Automated Novelty Detection in Industrial Systems
Studies in Computational Intelligence 116, 2008, pp. 269-296 [PDF]