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Digital Health Publications

Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.

Ghiasi S, Zhu T, Lu P, Hagenah J, Khanh PNQ et al. (2022), Sensors (Basel, Switzerland), 22(10), 3866

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@article{sepsismortality-2022/5,
  title={Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.},
  author={Ghiasi S, Zhu T, Lu P, Hagenah J, Khanh PNQ et al.},
  journal={Sensors (Basel, Switzerland)},
  volume={22},
  pages={3866},
  year = "2022"
}

Weak Monotonicity with Trend Analysis for Unsupervised Feature Evaluation

Clifton D & Lu L (2022), IEEE Transactions on Cybernetics

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@article{weakmonotonicit-2022/5,
  title={Weak Monotonicity with Trend Analysis for Unsupervised Feature Evaluation},
  author={Clifton D & Lu L},
  journal={IEEE Transactions on Cybernetics},
  publisher={Institute of Electrical and Electronics Engineers},
  year = "2022"
}

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.

Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS et al. (2022), Nature medicine, 28(5), 924-933

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@article{reportingguidel-2022/5,
  title={Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.},
  author={Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS et al.},
  journal={Nature medicine},
  volume={28},
  pages={924-933},
  year = "2022"
}

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.

Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS et al. (2022), BMJ, 377, e070904

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@article{reportingguidel-2022/5,
  title={Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.},
  author={Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS et al.},
  journal={BMJ},
  volume={377},
  pages={e070904},
  year = "2022"
}

Weak Monotonicity With Trend Analysis for Unsupervised Feature Evaluation.

Lu L, Tan Y, Oetomo D, Mareels I & Clifton DA (2022), IEEE transactions on cybernetics, PP

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@article{weakmonotonicit-2022/5,
  title={Weak Monotonicity With Trend Analysis for Unsupervised Feature Evaluation. },
  author={Lu L, Tan Y, Oetomo D, Mareels I & Clifton DA},
  journal={IEEE transactions on cybernetics},
  volume={PP},
  year = "2022"
}

A deep learning approach for the assessment of signal quality of non-invasive foetal electrocardiography

Mertes G, Long Y, Liu Z, Li Y, Yang Y et al. (2022), Sensors, 22(9)

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@article{adeeplearningap-2022/4,
  title={A deep learning approach for the assessment of signal quality of non-invasive foetal electrocardiography},
  author={Mertes G, Long Y, Liu Z, Li Y, Yang Y et al.},
  journal={Sensors},
  volume={22},
  number={3303},
  publisher={MDPI},
  year = "2022"
}

Multivariable prediction models for atrial fibrillation after cardiac surgery: a systematic review protocol

Fields KG, Ma J, Petrinic T, Alhassan H, Eze A et al. (2022)

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BibTeX
@misc{multivariablepr-2022/4,
  title={Multivariable prediction models for atrial fibrillation after cardiac surgery: a systematic review protocol},
  author={Fields KG, Ma J, Petrinic T, Alhassan H, Eze A et al.},
  year = "2022"
}

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.

Chun M, Clarke R, Zhu T, Clifton D, Bennett DA et al. (2022), Stroke and vascular neurology

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@article{developmentvali-2022/3,
  title={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.},
  author={Chun M, Clarke R, Zhu T, Clifton D, Bennett DA et al.},
  journal={Stroke and vascular neurology},
  year = "2022"
}

Real-world evaluation of AI driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions

Soltan A, Yang J, Pattanshetty R, Novak A, Yang Y et al. (2022), Lancet Digital Health, 4(4), E266-E278

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@article{realworldevalua-2022/3,
  title={Real-world evaluation of AI driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions},
  author={Soltan A, Yang J, Pattanshetty R, Novak A, Yang Y et al.},
  journal={Lancet Digital Health},
  volume={4},
  pages={E266-E278},
  publisher={Elsevier},
  year = "2022"
}

Standardising the assessment of caesarean birth using an Oxford caesarean prediction score for mothers with gestational diabetes

Lu H, Hirst J, Yang J, Mackillop L & Clifton D (2022), Healthcare Technology Letters, 9(1-2), 1-8

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@article{standardisingth-2022/3,
  title={Standardising the assessment of caesarean birth using an Oxford caesarean prediction score for mothers with gestational diabetes },
  author={Lu H, Hirst J, Yang J, Mackillop L & Clifton D},
  journal={Healthcare Technology Letters},
  volume={9},
  pages={1-8},
  publisher={Institution of Engineering and Technology},
  year = "2022"
}

Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening

Soltan AAS, Yang J, Pattanshetty R, Novak A, Yang Y et al. (2022), Lancet Digit Health

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@article{realworldevalua-2022/3,
  title={Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening},
  author={Soltan AAS, Yang J, Pattanshetty R, Novak A, Yang Y et al.},
  journal={Lancet Digit Health},
  publisher={Elsevier},
  year = "2022"
}

Digital health policy and programs for hospital care in Vietnam: scoping review

Tran DM, Thwaites CL, Van Nuil JI, McKnight J, Luu AP et al. (2022), Journal of Medical Internet Research, 24(2), e32392

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@article{digitalhealthpo-2022/2,
  title={Digital health policy and programs for hospital care in Vietnam: scoping review},
  author={Tran DM, Thwaites CL, Van Nuil JI, McKnight J, Luu AP et al.},
  journal={Journal of Medical Internet Research},
  volume={24},
  pages={e32392},
  publisher={JMIR Publications},
  year = "2022"
}

Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias.

Gallifant J, Zhang J, Del Pilar Arias Lopez M, Zhu T, Camporota L et al. (2022), British journal of anaesthesia, 128(2), 343-351

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@article{artificialintel-2022/2,
  title={Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias.},
  author={Gallifant J, Zhang J, Del Pilar Arias Lopez M, Zhu T, Camporota L et al.},
  journal={British journal of anaesthesia},
  volume={128},
  pages={343-351},
  year = "2022"
}

A deep active learning approach to the automatic classification of volcano-seismic events

Manley G, Mather T, Pyle D & Clifton D (2022), Frontiers in Earth Science, 10

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@article{adeepactivelear-2022/2,
  title={A deep active learning approach to the automatic classification of volcano-seismic events},
  author={Manley G, Mather T, Pyle D & Clifton D},
  journal={Frontiers in Earth Science},
  volume={10},
  number={807926},
  publisher={Frontiers Media},
  year = "2022"
}

Digital health policy and programs for hospital care in Vietnam: scoping review

Tran DM, Thwaites CL, Van Nuil JI, McKnight J, Luu AP et al. (2022), Journal of Medical Internet Research, 24(2), e32392

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@article{digitalhealthpo-2022/2,
  title={Digital health policy and programs for hospital care in Vietnam: scoping review},
  author={Tran DM, Thwaites CL, Van Nuil JI, McKnight J, Luu AP et al.},
  journal={Journal of Medical Internet Research},
  volume={24},
  pages={e32392},
  publisher={JMIR Publications},
  year = "2022"
}

Supporting People With Type 2 Diabetes in the Effective Use of Their Medicine Through Mobile Health Technology Integrated With Clinical Care to Reduce Cardiovascular Risk: Protocol for an Effectiveness and Cost-effectiveness Randomized Controlled Trial.

Farmer A, Jones L, Newhouse N, Kenning C, Williams N et al. (2022), JMIR research protocols, 11(2), e32918

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@article{supportingpeopl-2022/2,
  title={Supporting People With Type 2 Diabetes in the Effective Use of Their Medicine Through Mobile Health Technology Integrated With Clinical Care to Reduce Cardiovascular Risk: Protocol for an Effectiveness and Cost-effectiveness Randomized Controlled Trial.},
  author={Farmer A, Jones L, Newhouse N, Kenning C, Williams N et al.},
  journal={JMIR research protocols},
  volume={11},
  pages={e32918},
  year = "2022"
}

Non-contact physiological monitoring of post-operative patients in the Intensive Care Unit

Jorge J, Villarroel M, Tomlinson H, Gibson O, Darbyshire J et al. (2022), npj Digital Medicine, 5(2022)

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@article{noncontactphysi-2022/1,
  title={Non-contact physiological monitoring of post-operative patients in the Intensive Care Unit},
  author={Jorge J, Villarroel M, Tomlinson H, Gibson O, Darbyshire J et al.},
  journal={npj Digital Medicine},
  volume={5},
  number={4},
  publisher={Springer Nature},
  year = "2022"
}

CROCS: Clustering and revival of cardiac signals based on patient disease class, sex, and age

Kiyasseh D, Zhu T & Clifton D (2021), Advances in Neural Information Processing Systems 34 (NeurIPS 2021)

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@inproceedings{crocsclustering-2021/12,
  title={CROCS: Clustering and revival of cardiac signals based on patient disease class, sex, and age},
  author={Kiyasseh D, Zhu T & Clifton D},
  booktitle={35th Conference on Neural Information Processing Systems (NeurIPS 2021)},
  year = "2021"
}

Dynamic neural graphs based federated reptile for semi-supervised multi-tasking in healthcare applications

Thakur A, Sharma P & Clifton DA (2021), IEEE Journal of Biomedical and Health Informatics, 1-1

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@article{dynamicneuralgr-2021/12,
  title={Dynamic neural graphs based federated reptile for semi-supervised multi-tasking in healthcare applications},
  author={Thakur A, Sharma P & Clifton DA},
  journal={IEEE Journal of Biomedical and Health Informatics},
  pages={1-1},
  publisher={IEEE},
  year = "2021"
}

CROCS: Clustering and revival of cardiac signals based on patient disease class, sex, and age

Kiyasseh D, Zhu T & Clifton D (2021), Advances in Neural Information Processing Systems 34 (NeurIPS 2021)

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BibTeX
@inproceedings{crocsclustering-2021/12,
  title={CROCS: Clustering and revival of cardiac signals based on patient disease class, sex, and age},
  author={Kiyasseh D, Zhu T & Clifton D},
  booktitle={35th Conference on Neural Information Processing Systems (NeurIPS 2021)},
  year = "2021"
}

Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge

Bishop J, Javed H, EL-Bouri R, Zhu T, Taylor T et al. (2021), PLoS One, 16(11)

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@article{improvingpatien-2021/11,
  title={Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge},
  author={Bishop J, Javed H, EL-Bouri R, Zhu T, Taylor T et al.},
  journal={PLoS One},
  volume={16},
  number={e0260476},
  publisher={Public Library of Science},
  year = "2021"
}

Interactive Effects of HRV and P-QRS-T on the Power Density Spectra of ECG Signals.

Xiang T, Ji N, Clifton DA, Lu L & Zhang Y-T (2021), IEEE journal of biomedical and health informatics, 25(11), 4163-4174

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@article{interactiveeffe-2021/11,
  title={Interactive Effects of HRV and P-QRS-T on the Power Density Spectra of ECG Signals.},
  author={Xiang T, Ji N, Clifton DA, Lu L & Zhang Y-T},
  journal={IEEE journal of biomedical and health informatics},
  volume={25},
  pages={4163-4174},
  year = "2021"
}

Machine learning approaches to identifying changes in eruptive state using multi-parameter datasets from the 2006 eruption of Augustine Volcano, Alaska

Manley G, Mather T, Pyle D & Clifton D (2021), Journal of Geophysical Research. Solid Earth

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@article{machinelearning-2021/11,
  title={Machine learning approaches to identifying changes in eruptive state using multi-parameter datasets from the 2006 eruption of Augustine Volcano, Alaska},
  author={Manley G, Mather T, Pyle D & Clifton D},
  journal={Journal of Geophysical Research. Solid Earth},
  publisher={American Geophysical Union},
  year = "2021"
}

Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge

Bishop J, Javed H, EL-Bouri R, Zhu T, Taylor T et al. (2021), PLoS One, 16(11)

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@article{improvingpatien-2021/11,
  title={Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge},
  author={Bishop J, Javed H, EL-Bouri R, Zhu T, Taylor T et al.},
  journal={PLoS One},
  volume={16},
  number={e0260476},
  publisher={Public Library of Science},
  year = "2021"
}

DeepMI: Deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time

Tadesse GA, Javed H, Weldemariam K, Liu Y, Liu J et al. (2021), Artificial Intelligence in Medicine, 121

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@article{deepmideepmulti-2021/10,
  title={DeepMI: Deep multi-lead ECG fusion for identifying myocardial infarction and its occurrence-time},
  author={Tadesse GA, Javed H, Weldemariam K, Liu Y, Liu J et al.},
  journal={Artificial Intelligence in Medicine},
  volume={121},
  number={102192},
  publisher={Elsevier},
  year = "2021"
}

Publisher Correction: Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults.

Chun M, Clarke R, Zhu T, Clifton D, Bennett D et al. (2021), Scientific reports, 11(1), 20874

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@article{publishercorrec-2021/10,
  title={Publisher Correction: Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults.},
  author={Chun M, Clarke R, Zhu T, Clifton D, Bennett D et al.},
  journal={Scientific reports},
  volume={11},
  pages={20874},
  year = "2021"
}

Machine learning to predict the source of campylobacteriosis using whole genome data.

Arning N, Sheppard SK, Bayliss S, Clifton DA & Wilson DJ (2021), PLoS genetics, 17(10), e1009436

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@article{machinelearning-2021/10,
  title={Machine learning to predict the source of campylobacteriosis using whole genome data.},
  author={Arning N, Sheppard SK, Bayliss S, Clifton DA & Wilson DJ},
  journal={PLoS genetics},
  volume={17},
  pages={e1009436},
  year = "2021"
}

Publisher Correction: Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults.

Chun M, Clarke R, Zhu T, Clifton D, Bennett D et al. (2021), Scientific reports, 11(1), 20874

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@article{publishercorrec-2021/10,
  title={Publisher Correction: Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults.},
  author={Chun M, Clarke R, Zhu T, Clifton D, Bennett D et al.},
  journal={Scientific reports},
  volume={11},
  pages={20874},
  year = "2021"
}

Experiences of current vital signs monitoring practices and views of wearable monitoring: A qualitative study in patients and nurses

Areia C, King E, Ede J, Young L, Tarassenko L et al. (2021), Journal of Advanced Nursing

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@article{experiencesofcu-2021/10,
  title={Experiences of current vital signs monitoring practices and views of wearable monitoring: A qualitative study in patients and nurses},
  author={Areia C, King E, Ede J, Young L, Tarassenko L et al.},
  journal={Journal of Advanced Nursing},
  publisher={Wiley},
  year = "2021"
}

Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults

Chun M, Clarke R, Zhu T, Clifton J, Bennett D et al. (2021), Scientific Reports, 11(2021)

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@article{utilityofsingle-2021/9,
  title={Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults},
  author={Chun M, Clarke R, Zhu T, Clifton J, Bennett D et al.},
  journal={Scientific Reports},
  volume={11},
  number={17575 },
  publisher={Springer Nature},
  year = "2021"
}

Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults

Chun M, Clarke R, Zhu T, Clifton J, Bennett D et al. (2021), Scientific Reports, 11(2021)

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@article{utilityofsingle-2021/9,
  title={Utility of single versus sequential measurements of risk factors for prediction of stroke in Chinese adults},
  author={Chun M, Clarke R, Zhu T, Clifton J, Bennett D et al.},
  journal={Scientific Reports},
  volume={11},
  number={17575 },
  publisher={Springer Nature},
  year = "2021"
}

A chest patch for continuous vital sign monitoring: clinical validation study during movement and controlled hypoxia

Morgado Areia C, Santos M, Vollam S, Pimentel M, Young L et al. (2021), Journal of Medical Internet Research, 23(9)

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@article{achestpatchforc-2021/9,
  title={A chest patch for continuous vital sign monitoring: clinical validation study during movement and controlled hypoxia},
  author={Morgado Areia C, Santos M, Vollam S, Pimentel M, Young L et al.},
  journal={Journal of Medical Internet Research},
  volume={23},
  number={e27547},
  publisher={JMIR Publications},
  year = "2021"
}

Estimated Prevalence of Hypertension and Undiagnosed Hypertension in a Large Inpatient Population: A Cross-sectional Observational Study.

Mahdi A, Armitage LC, Tarassenko L & Watkinson P (2021), American journal of hypertension, 34(9), 963-972

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@article{estimatedpreval-2021/9,
  title={Estimated Prevalence of Hypertension and Undiagnosed Hypertension in a Large Inpatient Population: A Cross-sectional Observational Study.},
  author={Mahdi A, Armitage LC, Tarassenko L & Watkinson P},
  journal={American journal of hypertension},
  volume={34},
  pages={963-972},
  year = "2021"
}

The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: a systematic review and meta-analysis

Areia C, Biggs C, Santos M, Thurley N, Gerry S et al. (2021), Critical Care, 25(1)

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@article{theimpactofwear-2021/9,
  title={The impact of wearable continuous vital sign monitoring on deterioration detection and clinical outcomes in hospitalised patients: a systematic review and meta-analysis},
  author={Areia C, Biggs C, Santos M, Thurley N, Gerry S et al.},
  journal={Critical Care},
  volume={25},
  number={351},
  publisher={BioMed Central},
  year = "2021"
}

Real-world evaluation of AI-driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions

Soltan A, Yang J, Pattanshetty R, Novak A, Yang Y et al. (2021)

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@misc{realworldevalua-2021/8,
  title={Real-world evaluation of AI-driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions},
  author={Soltan A, Yang J, Pattanshetty R, Novak A, Yang Y et al.},
  year = "2021"
}

Data pre-processing using neural processes for modelling personalised vital-sign time-series data

Sharma P, Shamout F, Abrol V & Clifton D (2021), IEEE Journal of Biomedical and Health Informatics

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@article{datapreprocessi-2021/8,
  title={Data pre-processing using neural processes for modelling personalised vital-sign time-series data},
  author={Sharma P, Shamout F, Abrol V & Clifton D},
  journal={IEEE Journal of Biomedical and Health Informatics},
  publisher={IEEE},
  year = "2021"
}

An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction

Yang Y, Walker TM, Kouchaki S, Wang C, Peto TEA et al. (2021), Briefings in Bioinformatics, 22(6)

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@article{anendtoendheter-2021/8,
  title={An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction},
  author={Yang Y, Walker TM, Kouchaki S, Wang C, Peto TEA et al.},
  journal={Briefings in Bioinformatics},
  volume={22},
  number={bbab299},
  publisher={Oxford University Press },
  year = "2021"
}

Interactive effects of HRV and P-QRS-T on the power density spectra of ECG signals

Xiang T, Nan J, Clifton D, Lu L & Zhang Y-T (2021), IEEE Journal of Biomedical and Health Informatics, 25(11), 4163-4174

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@article{interactiveeffe-2021/8,
  title={Interactive effects of HRV and P-QRS-T on the power density spectra of ECG signals},
  author={Xiang T, Nan J, Clifton D, Lu L & Zhang Y-T},
  journal={IEEE Journal of Biomedical and Health Informatics},
  volume={25},
  pages={4163-4174},
  publisher={IEEE},
  year = "2021"
}

Using machine learning to predict anticoagulation control in atrial fibrillation: a UK Clinical Practice Research Datalink study

CLIFTON D (2021), Informatics in Medicine Unlocked

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@article{usingmachinelea-2021/8,
  title={Using machine learning to predict anticoagulation control in atrial fibrillation: a UK Clinical Practice Research Datalink study},
  author={CLIFTON D},
  journal={Informatics in Medicine Unlocked},
  publisher={Elsevier},
  year = "2021"
}

A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.

Kiyasseh D, Zhu T & Clifton D (2021), Nature Communications, 12(1)

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@article{aclinicaldeeple-2021/7,
  title={A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.},
  author={Kiyasseh D, Zhu T & Clifton D},
  journal={Nature Communications},
  volume={12},
  number={4221},
  publisher={Springer Nature},
  year = "2021"
}

CLOCS: contrastive learning of cardiac signals across space, time, and patients

Kiyasseh D, Zhu T & Clifton D (2021), 5606-5615

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@article{clocscontrastiv-2021/7,
  title={CLOCS: contrastive learning of cardiac signals across space, time, and patients},
  author={Kiyasseh D, Zhu T & Clifton D},
  journal={},
  pages={5606-5615},
  publisher={Journal of Machine Learning Research},
  year = "2021"
}

BashTheBug: a crowd of volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates

Fowler P, Wright C, Spiers H, Zhu T, Baeten EML et al. (2021)

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@misc{bashthebugacrow-2021/7,
  title={BashTheBug: a crowd of volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates},
  author={Fowler P, Wright C, Spiers H, Zhu T, Baeten EML et al.},
  year = "2021"
}

CLOCS: contrastive learning of cardiac signals across space, time, and patients

Kiyasseh D, Zhu T & Clifton D (2021), 5606-5615

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@article{clocscontrastiv-2021/7,
  title={CLOCS: contrastive learning of cardiac signals across space, time, and patients},
  author={Kiyasseh D, Zhu T & Clifton D},
  journal={},
  pages={5606-5615},
  publisher={Journal of Machine Learning Research},
  year = "2021"
}

Forecasting COVID-19 severity by intelligent optical fingerprinting of blood samples

Faria SP, Carpinteiro C, Pinto V, Rodrigues SM, Alves J et al. (2021), Diagnostics, 11(8)

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@article{forecastingcovi-2021/7,
  title={Forecasting COVID-19 severity by intelligent optical fingerprinting of blood samples},
  author={Faria SP, Carpinteiro C, Pinto V, Rodrigues SM, Alves J et al.},
  journal={Diagnostics},
  volume={11},
  number={1309},
  publisher={MDPI},
  year = "2021"
}

A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.

Kiyasseh D, Zhu T & Clifton D (2021), Nature Communications, 12(1)

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@article{aclinicaldeeple-2021/7,
  title={A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.},
  author={Kiyasseh D, Zhu T & Clifton D},
  journal={Nature Communications},
  volume={12},
  number={4221},
  publisher={Springer Nature},
  year = "2021"
}

BashTheBug: a crowd of volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates

Fowler P, Wright C, Spiers H, Zhu T, Baeten EML et al. (2021)

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@misc{bashthebugacrow-2021/7,
  title={BashTheBug: a crowd of volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates},
  author={Fowler P, Wright C, Spiers H, Zhu T, Baeten EML et al.},
  year = "2021"
}

Deep learning classification of cardiomegaly using combined imaging and non-imaging ICU data

Grant D, Papiez B, Parsons G, Tarassenko L & Mahdi A (2021), Medical Image Understanding and Analysis. MIUA 2021, 547-558

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@inproceedings{deeplearningcla-2021/7,
  title={Deep learning classification of cardiomegaly using combined imaging and non-imaging ICU data},
  author={Grant D, Papiez B, Parsons G, Tarassenko L & Mahdi A},
  booktitle={25th UK Conference on Medical Image Understanding and Analysis},
  pages={547-558},
  year = "2021"
}

Prediction of monetary penalties for data protection cases in multiple languages

Ceross A & Zhu T (2021), Proceedings of the 18th International Conference on Artificial Intelligence and Law, ICAIL 2021, 185-189

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@inproceedings{predictionofmon-2021/6,
  title={Prediction of monetary penalties for data protection cases in multiple languages},
  author={Ceross A & Zhu T},
  pages={185-189},
  year = "2021"
}

Global seroprevalence of SARS-CoV-2 antibodies: a systematic review and meta-analysis

Bobrovitz N, Arora RK, Boucher E, Liu M, Donnici C et al. (2021), PLoS One, 16(6)

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@article{globalseropreva-2021/6,
  title={Global seroprevalence of SARS-CoV-2 antibodies: a systematic review and meta-analysis},
  author={Bobrovitz N, Arora RK, Boucher E, Liu M, Donnici C et al.},
  journal={PLoS One},
  volume={16},
  number={e0252617},
  publisher={Public Library of Science},
  year = "2021"
}

App-Based Versus Standard Six-Minute Walk Test in Pulmonary Hypertension: Mixed Methods Study.

Salvi D, Poffley E, Tarassenko L & Orchard E (2021), JMIR mHealth and uHealth, 9(6), e22748

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@article{appbasedversuss-2021/6,
  title={App-Based Versus Standard Six-Minute Walk Test in Pulmonary Hypertension: Mixed Methods Study.},
  author={Salvi D, Poffley E, Tarassenko L & Orchard E},
  journal={JMIR mHealth and uHealth},
  volume={9},
  pages={e22748},
  year = "2021"
}