{ "items": [ "\n\n
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\n Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective\n

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\n \n Conference paper\n \n

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You C. et al, (2023), Advances in Neural Information Processing Systems, 36

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\n Self-Aware SGD: Reliable Incremental Adaptation Framework For Clinical AI Models\n

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\n \n Journal article\n \n

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Thakur A. et al, (2023), IEEE Journal of Biomedical and Health Informatics, 1 - 11

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\n SeroTracker-RoB: a decision rule-based algorithm for reproducible risk of bias assessment of seroprevalence studies.\n

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\n \n Journal article\n \n

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Bobrovitz N. et al, (2023), Research synthesis methods

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\n Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints\n

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\n \n Conference paper\n \n

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Rohanian O. et al, (2023), Proceedings of the Annual Meeting of the Association for Computational Linguistics, 62 - 78

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\n Continuous Patient State Attention Models\n

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\n \n Preprint\n \n

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Chauhan VK. et al, (2022)

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\n Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: A systematic review and meta-analysis of standardized population-based studies.\n

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\n \n Journal article\n \n

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Bergeri I. et al, (2022), PLoS medicine, 19

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\n Serology Assays Used in SARS-CoV-2 Seroprevalence Surveys Worldwide: A Systematic Review and Meta-Analysis of Assay Features, Testing Algorithms, and Performance. \n

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\n \n Journal article\n \n

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Ma X. et al, (2022), Vaccines, 10

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\n Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network.\n

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\n \n Journal article\n \n

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Lu P. et al, (2022), IEEE transactions on bio-medical engineering, PP

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\n Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing ofM. tuberculosis\n

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\n \n Journal article\n \n

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(2022), European Respiratory Journal, 60, 2200239 - 2200239

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

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\n \n Journal article\n \n

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Vasey B. et al, (2022), Nature Medicine, 28, 2218 - 2218

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\n Timeliness of reporting of SARS-CoV-2 seroprevalence results and their utility for infectious disease surveillance.\n

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\n \n Journal article\n \n

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Donnici C. et al, (2022), Epidemics, 41

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\n COPER: Continuous Patient State Perceiver\n

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\n \n Conference paper\n \n

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Chauhan VK. et al, (2022), 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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\n RapiD_AI: A framework for Rapidly Deployable AI for novel disease & pandemic preparedness\n

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\n \n Preprint\n \n

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Youssef A. et al, (2022)

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\n Spectrum Estimation of Heart Rate Variability Using Low-rank Matrix Completion\n

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\n \n Conference paper\n \n

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Lu L. et al, (2022), 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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\n Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing.\n

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\n \n Journal article\n \n

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Lu P. et al, (2022), Sensors (Basel, Switzerland), 22

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\n High fluoroquinolone resistance proportions among multidrug-resistant tuberculosis driven by dominant L2 Mycobacterium tuberculosis clones in the Mumbai Metropolitan Region\n

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\n \n Journal article\n \n

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Dreyer V. et al, (2022), Genome Medicine, 14

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\n RapiD_AI: A framework for Rapidly Deployable AI for novel disease & pandemic preparedness\n

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\n \n Preprint\n \n

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Youssef A. et al, (2022)

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\n Genome-wide association studies of global Mycobacterium tuberculosis resistance to 13 antimicrobials in 10,228 genomes identify new resistance mechanisms\n

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\n \n Journal article\n \n

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(2022), PLOS Biology, 20, e3001755 - e3001755

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\n Prognostic model for atrial fibrillation after cardiac surgery: a UK cohort study\n

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\n \n Journal article\n \n

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Chung S-C. et al, (2022), Clinical Research in Cardiology

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\n Omicron-associated changes in SARS-CoV-2 symptoms in the United Kingdom\n

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\n \n Journal article\n \n

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Vihta1 KD. et al, (2022), Clinical Infectious Diseases

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\n 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.\n

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\n \n Journal article\n \n

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Chun M. et al, (2022), Stroke and vascular neurology, 7, 328 - 336

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\n SARS-CoV-2 infection in Africa: a systematic review and meta-analysis of standardised seroprevalence studies, from January 2020 to December 2021.\n

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\n \n Journal article\n \n

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Lewis HC. et al, (2022), BMJ global health, 7

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\n An independent external validation of the QRISK3 cardiovascular risk prediction model applied to UK Biobank participants\n

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\n \n Preprint\n \n

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Parsons RE. et al, (2022)

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\n Combining Machine Learning with Cox models for identifying risk factors for incident post-menopausal breast cancer in the UK Biobank\n

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\n \n Preprint\n \n

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Liu X. et al, (2022)

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\n Machine Learning-Based Risk Stratification for Gestational Diabetes Management\n

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\n \n Journal article\n \n

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Yang J. et al, (2022), Sensors, 22, 4805 - 4805

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\n Machine Learning-Based Risk Stratification for Gestational Diabetes Management\n

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\n \n Preprint\n \n

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Yang J. et al, (2022)

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\n 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.\n

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\n \n Journal article\n \n

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Hill NR. et al, (2022), European heart journal. Digital health, 3, 195 - 204

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

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\n \n Journal article\n \n

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Ghiasi S. et al, (2022), Sensors (Basel, Switzerland), 22

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\n Omicron-associated changes in SARS-CoV-2 symptoms in the United Kingdom\n

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\n \n Preprint\n \n

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Vihta K-D. et al, (2022)

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\n Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: Insights from Rapid COVID-19 Diagnosis by Adversarial Learning\n

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Yang J. et al, (2022)

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\n Clinical Prediction Models in Epidemiological Studies: Lessons from the Application of QRISK3 to UK Biobank Data\n

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\n \n Journal article\n \n

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Parsons RE. et al, (2022), Journal of Data Science, 1 - 13

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\n Continual learning of longitudinal health records\n

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\n \n Conference paper\n \n

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Armstrong J. and Clifton DA., (2022), 2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22)

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\n Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis\n

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Creagh A. et al, (2022)

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\n Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England.\n

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\n \n Journal article\n \n

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Hill NR. et al, (2022), Journal of medical economics, 25, 974 - 983

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\n Incremental Trainable Parameter Selection in Deep Neural Networks\n

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\n \n Journal article\n \n

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Thakur A. et al, (2022), IEEE Transactions on Neural Networks and Learning Systems

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\n Machine Learning Generalizability Across Healthcare Settings: Insights from multi-site COVID-19 screening\n

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Yang J. et al, (2022)

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Ceritli T. et al, (2022), 2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22)

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Ceritli T. et al, (2022), Proceedings of Machine Learning Research, 184, 41 - 53

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\n Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression\n

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\n \n Conference paper\n \n

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Nouriborji M. et al, (2022), SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop, 1071 - 1077

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\n Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions\n

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\n \n Conference paper\n \n

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Liu F. et al, (2022), Advances in Neural Information Processing Systems, 35

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\n SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals\n

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Kiyasseh D. et al, (2022), INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162

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\n vital_sqi: A Python package for physiological signal quality control.\n

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\n \n Journal article\n \n

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Le V-KD. et al, (2022), Frontiers in physiology, 13

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\n Wearable Vital Signs Monitoring for Patients with Asthma: A Review\n

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Taylor L. et al, (2022), IEEE Sensors Journal, 1 - 1

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\n Predicting risk of pancreatic cancer in individuals with new-onset type-2 diabetes in primary care: protocol for the development and validation of a clinical prediction model (QPancreasD)\n

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Tan PS. et al, (2021)

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

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Chun M. et al, (2021), Scientific reports, 11

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

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Soltan AAS. et al, (2021)

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\n Epidemiological cutoff values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis\n

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Fowler PW. et al, (2021)

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

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\n \n Preprint\n \n

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Arning N. et al, (2021)

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

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Gordon J. et al, (2021), Informatics in Medicine Unlocked, 25

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\n Early detection of physiological deterioration in post-surgical patients using wearable technology combined with an integrated monitoring system: a pre- and post-interventional study\n

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Watkinson PJ. et al, (2020)

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