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David A. Clifton
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2020 – today
- 2024
- [j82]Vinod Kumar Chauhan
, Jiandong Zhou, Ping Lu, Soheila Molaei, David A. Clifton:
A brief review of hypernetworks in deep learning. Artif. Intell. Rev. 57(9): 250 (2024) - [j81]Liangyi Lyu
An improved deep regression model with state space reconstruction for continuous blood pressure estimation. Comput. Electr. Eng. 118: 109319 (2024) - [j80]Soheila Molaei, Nima Ghanbari Bousejin, Ghadeer O. Ghosheh, Anshul Thakur, Vinod Kumar Chauhan
CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks. J. Heal. Informatics Res. 8(3): 555-575 (2024) - [j79]Soheila Molaei
Temporal dynamics unleashed: Elevating variational graph attention. Knowl. Based Syst. 299: 112110 (2024) - [j78]Vinod Kumar Chauhan
Continuous patient state attention model for addressing irregularity in electronic health records. BMC Medical Informatics Decis. Mak. 24(1): 117 (2024) - [j77]Jenny Yang, Hagen Triendl, Andrew A. S. Soltan
Addressing label noise for electronic health records: insights from computer vision for tabular data. BMC Medical Informatics Decis. Mak. 24(1): 183 (2024) - [j76]Jenny Yang
Deep reinforcement learning for multi-class imbalanced training: applications in healthcare. Mach. Learn. 113(5): 2655-2674 (2024) - [j75]Andrew P. Creagh
Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis. npj Digit. Medicine 7(1) (2024) - [j74]Hang Yuan
Self-supervised learning for human activity recognition using 700,000 person-days of wearable data. npj Digit. Medicine 7(1) (2024) - [j73]Rushuang Zhou
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3305-3320 (2024) - [j72]Bang Yang
ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation. IEEE Trans. Pattern Anal. Mach. Intell. 46(8): 5712-5724 (2024) - [j71]Yanting Shen
AutoNet-Generated Deep Layer-Wise Convex Networks for ECG Classification. IEEE Trans. Pattern Anal. Mach. Intell. 46(10): 6542-6558 (2024) - [j70]Zhangdaihong Liu
DuKA: A Dual-Keyless-Attention Model for Multi-Modality EHR Data Fusion and Organ Failure Prediction. IEEE Trans. Biomed. Eng. 71(4): 1247-1256 (2024) - [j69]Zhangdaihong Liu
Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography Monitoring. IEEE J. Biomed. Health Informatics 28(3): 1321-1330 (2024) - [j68]Anshul Thakur
Incremental Trainable Parameter Selection in Deep Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 35(5): 6478-6491 (2024) - [c63]Soheila Molaei, Anshul Thakur, Ghazaleh Niknam, Andrew A. S. Soltan, Hadi Zare, David A. Clifton:
Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks. AISTATS 2024: 1342-1350 - [c62]Vinod Kumar Chauhan, Jiandong Zhou, Ghadeer O. Ghosheh, Soheila Molaei, David A. Clifton:
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation. AISTATS 2024: 3529-3537 - [c61]Shreyank N. Gowda, Boyan Gao, David A. Clifton:
FE-Adapter: Adapting Image-Based Emotion Classifiers to Videos. FG 2024: 1-6 - [c60]Shreyank N. Gowda, David A. Clifton:
Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring. MICCAI (11) 2024: 426-436 - [i52]Omid Rohanian, Mohammadmahdi Nouriborji, David A. Clifton:
Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing. CoRR abs/2401.00579 (2024) - [i51]Niall Taylor, Upamanyu Ghose, Omid Rohanian, Mohammadmahdi Nouriborji, Andrey Kormilitzin, David A. Clifton, Alejo J. Nevado-Holgado:
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks. CoRR abs/2402.10597 (2024) - [i50]James Anibal, Hannah Huth, Ming Li, Lindsey Hazen, Yen Minh Lam, Nguyen Thi Thu Hang, Michael Kleinman, Shelley Ost, Christopher Jackson, Laura Sprabery, Cheran Elangovan, Balaji Krishnaiah, Lee Akst, Ioan Lina, Iqbal Elyazar, Lenny Ekwati, Stefan Jansen, Richard Nduwayezu, Charisse Garcia, Jeffrey Plum, Jacqueline Brenner, Miranda Song, Emily Ricotta, David A. Clifton, Louise Thwaites, Yael Bensoussan, Bradford Wood:
Voice EHR: Introducing Multimodal Audio Data for Health. CoRR abs/2404.01620 (2024) - [i49]Andrew Liu, Hongjian Zhou, Yining Hua, Omid Rohanian, Lei A. Clifton, David A. Clifton:
Large Language Models in Healthcare: A Comprehensive Benchmark. CoRR abs/2405.00716 (2024) - [i48]Vinod Kumar Chauhan
Sample Selection Bias in Machine Learning for Healthcare. CoRR abs/2405.07841 (2024) - [i47]Rushuang Zhou
Computation-Efficient Semi-Supervised Learning for ECG-based Cardiovascular Diseases Detection. CoRR abs/2406.14377 (2024) - [i46]Xingrun Xing, Boyan Gao, Zheng Zhang, David A. Clifton, Shitao Xiao, Li Du, Guoqi Li, Jiajun Zhang:
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking. CoRR abs/2407.04752 (2024) - [i45]Omid Rohanian, Mohammadmahdi Nouriborji, Olena Seminog, Rodrigo Furst, Thomas Mendy, Shanthi Levanita, Zaharat Kadri-Alabi, Nusrat Jabin, Daniela Toale, Georgina S. Humphreys, Emilia Antonio, Adrian Bucher, Alice Norton, David A. Clifton:
Rapid Biomedical Research Classification: The Pandemic PACT Advanced Categorisation Engine. CoRR abs/2407.10086 (2024) - [i44]Shreyank N. Gowda, David A. Clifton:
Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring. CoRR abs/2407.16264 (2024) - [i43]Shreyank N. Gowda, David A. Clifton:
CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation. CoRR abs/2408.00181 (2024) - [i42]Shreyank N. Gowda, Boyan Gao, David A. Clifton:
FE-Adapter: Adapting Image-based Emotion Classifiers to Videos. CoRR abs/2408.02421 (2024) - [i41]Yining Hua, Hongbin Na, Zehan Li, Fenglin Liu, Xiao Fang, David A. Clifton, John Torous:
Applying and Evaluating Large Language Models in Mental Health Care: A Scoping Review of Human-Assessed Generative Tasks. CoRR abs/2408.11288 (2024) - 2023
- [j67]Omid Rohanian, Mohammadmahdi Nouriborji, Samaneh Kouchaki
On the effectiveness of compact biomedical transformers. Bioinform. 39(3) (2023) - [j66]Ghazaleh Niknam
Graph representation learning based on deep generative gaussian mixture models. Neurocomputing 523: 157-169 (2023) - [j65]Jenny Yang
Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning. Nat. Mac. Intell. 5(8): 884-894 (2023) - [j64]Ghazaleh Niknam
DyVGRNN: DYnamic mixture Variational Graph Recurrent Neural Networks. Neural Networks 165: 596-610 (2023) - [j63]Fenglin Liu
A medical multimodal large language model for future pandemics. npj Digit. Medicine 6 (2023) - [j62]Jenny Yang
An adversarial training framework for mitigating algorithmic biases in clinical machine learning. npj Digit. Medicine 6 (2023) - [j61]Peng Xu
Multimodal Learning With Transformers: A Survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(10): 12113-12132 (2023) - [j60]Atieh Khodadadi
Improving Diagnostics with Deep Forest Applied to Electronic Health Records. Sensors 23(14): 6571 (2023) - [j59]Ping Lu
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 23(18): 7705 (2023) - [j58]Huiqi Y. Lu
A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus. Sensors 23(18): 7990 (2023) - [j57]Ping Lu
Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network. IEEE Trans. Biomed. Eng. 70(4): 1340-1350 (2023) - [j56]Zhangdaihong Liu
Patient Clustering for Vital Organ Failure Using ICD Code With Graph Attention. IEEE Trans. Biomed. Eng. 70(8): 2329-2337 (2023) - [j55]Lei Lu
Weak Monotonicity With Trend Analysis for Unsupervised Feature Evaluation. IEEE Trans. Cybern. 53(11): 6883-6895 (2023) - [j54]Omid Rohanian
Privacy-Aware Early Detection of COVID-19 Through Adversarial Training. IEEE J. Biomed. Health Informatics 27(3): 1249-1258 (2023) - [j53]Anshul Thakur
Self-Aware SGD: Reliable Incremental Adaptation Framework for Clinical AI Models. IEEE J. Biomed. Health Informatics 27(3): 1624-1634 (2023) - [j52]Dani Kiyasseh, Tingting Zhu, David A. Clifton:
PCPs: Patient Cardiac Prototypes to Probe AI-based Medical Diagnoses, Distill Datasets, and Retrieve Patients. Trans. Mach. Learn. Res. 2023 (2023) - [c59]Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania, Anshul Thakur, Tingting Zhu, David A. Clifton:
Adversarial De-confounding in Individualised Treatment Effects Estimation. AISTATS 2023: 837-849 - [c58]Omid Rohanian, Hannah Jauncey, Mohammadmahdi Nouriborji, Vinod Kumar, Bronner P. Gonçalves, Christiana Kartsonaki, Isaric Clinical Characterisation Group, Laura Merson, David A. Clifton:
Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints. BioNLP@ACL 2023: 62-78 - [c57]Mohammadmahdi Nouriborji, Omid Rohanian, Samaneh Kouchaki, David A. Clifton:
MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers. EACL 2023: 1153-1165 - [c56]Morteza Rohanian, Farhad Nooralahzadeh, Omid Rohanian, David A. Clifton, Michael Krauthammer
Disfluent Cues for Enhanced Speech Understanding in Large Language Models. EMNLP (Findings) 2023: 3676-3684 - [c55]Ziyun Li, Xinshao Wang, Neil M. Robertson, David A. Clifton, Christoph Meinel, Haojin Yang:
SMKD: Selective Mutual Knowledge Distillation. IJCNN 2023: 1-8 - [c54]Chenyu You, Weicheng Dai, Yifei Min, Fenglin Liu, David A. Clifton, S. Kevin Zhou, Lawrence H. Staib, James S. Duncan:
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective. NeurIPS 2023 - [c53]Marzia Hoque Tania, David A. Clifton:
Unleashing the Power of Federated Learning in Fragmented Digital Healthcare Systems: A Visionary Perspective. SKIMA 2023: 40-44 - [i40]Chenyu You, Weicheng Dai, Yifei Min, Fenglin Liu, Xiaoran Zhang, Chen Feng, David A. Clifton, S. Kevin Zhou, Lawrence Hamilton Staib, James S. Duncan:
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective. CoRR abs/2302.01735 (2023) - [i39]Omid Rohanian, Mohammadmahdi Nouriborji, Hannah Jauncey, Samaneh Kouchaki, Lei A. Clifton, Laura Merson, David A. Clifton:
Lightweight Transformers for Clinical Natural Language Processing. CoRR abs/2302.04725 (2023) - [i38]Taha Ceritli, Ghadeer O. Ghosheh, Vinod Kumar Chauhan
Synthesizing Mixed-type Electronic Health Records using Diffusion Models. CoRR abs/2302.14679 (2023) - [i37]Bang Yang, Fenglin Liu, Yuexian Zou, Xian Wu
ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation. CoRR abs/2303.06458 (2023) - [i36]Alexey Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu, David A. Clifton, Nigam H. Shah:
All models are local: time to replace external validation with recurrent local validation. CoRR abs/2305.03219 (2023) - [i35]Anshul Thakur, Tingting Zhu
Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention. CoRR abs/2305.03710 (2023) - [i34]Yujiang Wang, Anshul Thakur, Mingzhi Dong, Pingchuan Ma, Stavros Petridis, Li Shang, Tingting Zhu, David A. Clifton:
Is dataset condensation a silver bullet for healthcare data sharing? CoRR abs/2305.03711 (2023) - [i33]Vinod Kumar Chauhan
Dynamic Inter-treatment Information Sharing for Heterogeneous Treatment Effects Estimation. CoRR abs/2305.15984 (2023) - [i32]Vinod Kumar Chauhan
A Brief Review of Hypernetworks in Deep Learning. CoRR abs/2306.06955 (2023) - [i31]Rushuang Zhou, Lei Lu, Zijun Liu, Ting Xiang, Zhen Liang, David A. Clifton, Yining Dong, Yuan-Ting Zhang:
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction: A Multi-Dataset Study. CoRR abs/2306.10494 (2023) - [i30]Fengxiang Bie, Yibo Yang, Zhongzhu Zhou, Adam Ghanem, Minjia Zhang, Zhewei Yao, Xiaoxia Wu, Connor Holmes, Pareesa Ameneh Golnari, David A. Clifton, Yuxiong He, Dacheng Tao, Shuaiwen Leon Song:
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model. CoRR abs/2309.00810 (2023) - 2022
- [j51]Jenny Yang
Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening. npj Digit. Medicine 5 (2022) - [j50]Gert Mertes
A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography. Sensors 22(9): 3303 (2022) - [j49]Shadi Ghiasi, Tingting Zhu
Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries. Sensors 22(10): 3866 (2022) - [j48]Jenny Yang
Machine Learning-Based Risk Stratification for Gestational Diabetes Management. Sensors 22(13): 4805 (2022) - [j47]Ping Lu
Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing. Sensors 22(17): 6554 (2022) - [j46]Pulkit Sharma
Data Pre-Processing Using Neural Processes for Modeling Personalized Vital-Sign Time-Series Data. IEEE J. Biomed. Health Informatics 26(4): 1528-1537 (2022) - [j45]Anshul Thakur
Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications. IEEE J. Biomed. Health Informatics 26(4): 1761-1772 (2022) - [c52]Jacob Armstrong, David A. Clifton:
Continual learning of longitudinal health records. BHI 2022: 1-6 - [c51]Taha Ceritli, Andrew P. Creagh, David A. Clifton:
Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling. BHI 2022: 1-8 - [c50]Vinod Kumar Chauhan
COPER: Continuous Patient State Perceiver. BHI 2022: 1-4 - [c49]Lei Lu
Spectrum Estimation of Heart Rate Variability Using Low-rank Matrix Completion. BHI 2022: 1-4 - [c48]Taha Ceritli, Andrew P. Creagh, David A. Clifton:
Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling. Healthcare AI and COVID-19 Workshop 2022: 41-53 - [c47]Dani Kiyasseh, Tingting Zhu, David A. Clifton:
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac Signals. ICML 2022: 11302-11340 - [c46]Peng Jin, Jinfa Huang, Fenglin Liu, Xian Wu, Shen Ge, Guoli Song, David A. Clifton, Jie Chen:
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations. NeurIPS 2022 - [c45]Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge, Zhangdaihong Liu, Xu Sun, Yang Yang, David A. Clifton:
Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions. NeurIPS 2022 - [c44]Mohammadmahdi Nouriborji, Omid Rohanian, David A. Clifton:
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression. SemEval@NAACL 2022: 1071-1077 - [e1]Peng Xu, Tingting Zhu, Pengkai Zhu, David A. Clifton, Danielle Belgrave, Yuanting Zhang:
Proceedings of the 1st Workshop on Healthcare AI and COVID-19, 22 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research 184, PMLR 2022 [contents] - [d1]Heloise Greeff, A. Manandhar, Patrick Thomson, Robert Hope, David A. Clifton:
Condition Monitoring for Handpumps - vibration data. IEEE DataPort, 2022 - [i29]Omid Rohanian, Samaneh Kouchaki, Andrew A. S. Soltan
Privacy-aware Early Detection of COVID-19 through Adversarial Training. CoRR abs/2201.03004 (2022) - [i28]Shuhao Cao, Peng Xu, David A. Clifton:
How to Understand Masked Autoencoders. CoRR abs/2202.03670 (2022) - [i27]Mohammadmahdi Nouriborji, Omid Rohanian, David A. Clifton:
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression. CoRR abs/2204.00556 (2022) - [i26]Jenny Yang, Rasheed El-Bouri, Odhran O'Donoghue, Alexander S. Lachapelle, Andrew A. S. Soltan
Deep Reinforcement Learning for Multi-class Imbalanced Training. CoRR abs/2205.12070 (2022) - [i25]Hang Yuan, Shing Chan, Andrew P. Creagh, Catherine Tong, David A. Clifton, Aiden R. Doherty:
Self-supervised Learning for Human Activity Recognition Using 700, 000 Person-days of Wearable Data. CoRR abs/2206.02909 (2022) - [i24]Peng Xu, Xiatian Zhu, David A. Clifton:
Multimodal Learning with Transformers: A Survey. CoRR abs/2206.06488 (2022) - [i23]Xinshao Wang, Yang Hua, Elyor Kodirov, Sankha Subhra Mukherjee, David A. Clifton, Neil M. Robertson:
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State. CoRR abs/2207.00118 (2022) - [i22]Taha Ceritli, Andrew P. Creagh, David A. Clifton:
Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling. CoRR abs/2207.11846 (2022) - [i21]Vinod Kumar Chauhan, Anshul Thakur, Odhran O'Donoghue, David A. Clifton:
COPER: Continuous Patient State Perceiver. CoRR abs/2208.03196 (2022) - [i20]Omid Rohanian, Mohammadmahdi Nouriborji, Samaneh Kouchaki, David A. Clifton:
On the Effectiveness of Compact Biomedical Transformers. CoRR abs/2209.03182 (2022) - [i19]Mohammadmahdi Nouriborji, Omid Rohanian, Samaneh Kouchaki
MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers. CoRR abs/2210.06425 (2022) - [i18]Omid Rohanian, Hannah Jauncey, Mohammadmahdi Nouriborji, Bronner P. Gonçalves, Christiana Kartsonaki, Laura Merson, David A. Clifton:
Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints. CoRR abs/2210.09440 (2022) - [i17]Vinod Kumar Chauhan
Adversarial De-confounding in Individualised Treatment Effects Estimation. CoRR abs/2210.10530 (2022) - [i16]Fenglin Liu, Bang Yang, Chenyu You, Xian Wu
Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions. CoRR abs/2210.12777 (2022) - [i15]Peng Jin, Jinfa Huang, Fenglin Liu, Xian Wu
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations. CoRR abs/2211.11427 (2022) - 2021
- [j44]Yang Yang, Timothy M. Walker
An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction. Briefings Bioinform. 22(6) (2021) - [j43]Peter H. Charlton
An impedance pneumography signal quality index: Design, assessment and application to respiratory rate monitoring. Biomed. Signal Process. Control. 65: 102339 (2021) - [j42]Matthew Chun
Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults. J. Am. Medical Informatics Assoc. 28(8): 1719-1727 (2021) - [j41]Davide Morelli
SDNN24 Estimation from Semi-Continuous HR Measures. Sensors 21(4): 1463 (2021) - [j40]Xinyu Jiang
Neuromuscular Password-Based User Authentication. IEEE Trans. Ind. Informatics 17(4): 2641-2652 (2021) - [j39]Rasheed el-Bouri
Hospital Admission Location Prediction via Deep Interpretable Networks for the Year-Round Improvement of Emergency Patient Care. IEEE J. Biomed. Health Informatics 25(1): 289-300 (2021) - [j38]Nan Ji
Recommendation to Use Wearable-Based mHealth in Closed-Loop Management of Acute Cardiovascular Disease Patients During the COVID-19 Pandemic. IEEE J. Biomed. Health Informatics 25(4): 903-908 (2021) - [j37]Xinyu Jiang
Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification. IEEE J. Biomed. Health Informatics 25(4): 1070-1079 (2021) - [j36]Ting Xiang
Interactive Effects of HRV and P-QRS-T on the Power Density Spectra of ECG Signals. IEEE J. Biomed. Health Informatics 25(11): 4163-4174 (2021) - [c43]Xinshao Wang
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks. CVPR 2021: 752-761 - [c42]Dani Kiyasseh, Tingting Zhu, David A. Clifton:
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients. ICML 2021: 5606-5615 - [c41]Dani Kiyasseh, Tingting Zhu, David A. Clifton:
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age. NeurIPS 2021: 15557-15569 - [i14]Dani Kiyasseh, Tingting Zhu, David A. Clifton:
Let Your Heart Speak in its Mother Tongue: Multilingual Captioning of Cardiac Signals. CoRR abs/2103.11011 (2021) - [i13]Ziyun Li, Xinshao Wang, Haojin Yang, Di Hu, Neil Martin Robertson, David A. Clifton, Christoph Meinel:
Not All Knowledge Is Created Equal. CoRR abs/2106.01489 (2021) - [i12]Rasheed el-Bouri, Tingting Zhu, David A. Clifton:
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning. CoRR abs/2107.01707 (2021) - [i11]Jacob Armstrong, David A. Clifton:
Continual learning of longitudinal health records. CoRR abs/2112.11944 (2021) - 2020
- [j35]Alessio Rossi
A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults. Data 5(4): 91 (2020) - [j34]Alessio Rossi
Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts. Sensors 20(24): 7122 (2020) - [j33]Farah E. Shamout
Deep Interpretable Early Warning System for the Detection of Clinical Deterioration. IEEE J. Biomed. Health Informatics 24(2): 437-446 (2020) - [j32]Wei Chen
Guest Editorial: Integrative Sensor Networks, Informatics, and Modeling for Precision and Preventative Medicine. IEEE J. Biomed. Health Informatics 24(7): 1858-1859 (2020) - [j31]Girmaw Abebe Tadesse
Multi-Modal Diagnosis of Infectious Diseases in the Developing World. IEEE J. Biomed. Health Informatics 24(7): 2131-2141 (2020) - [j30]Dani Kiyasseh
PlethAugment: GAN-Based PPG Augmentation for Medical Diagnosis in Low-Resource Settings. IEEE J. Biomed. Health Informatics 24(11): 3226-3235 (2020) - [c40]Rasheed el-Bouri, David W. Eyre, Peter J. Watkinson, Tingting Zhu, David A. Clifton:
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location. ICML 2020: 2848-2857 - [i10]Dani Kiyasseh, Tingting Zhu
ALPS: Active Learning via Perturbations. CoRR abs/2004.09557 (2020) - [i9]Dani Kiyasseh, Tingting Zhu
CLOPS: Continual Learning of Physiological Signals. CoRR abs/2004.09578 (2020) - [i8]Dani Kiyasseh, Tingting Zhu
SoQal: Selective Oracle Questioning in Active Learning. CoRR abs/2004.10468 (2020) - [i7]Dani Kiyasseh, Tingting Zhu
CLOCS: Contrastive Learning of Cardiac Signals. CoRR abs/2005.13249 (2020) - [i6]Rasheed el-Bouri, David W. Eyre, Peter J. Watkinson, Tingting Zhu
Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location. CoRR abs/2007.01135 (2020) - [i5]Dani Kiyasseh, Tingting Zhu
PCPs: Patient Cardiac Prototypes. CoRR abs/2011.14227 (2020) - [i4]Dani Kiyasseh, Tingting Zhu
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes. CoRR abs/2011.14230 (2020)
2010 – 2019
- 2019
- [j29]Tingting Zhu
Patient-Specific Physiological Monitoring and Prediction Using Structured Gaussian Processes. IEEE Access 7: 58094-58103 (2019) - [j28]Samaneh Kouchaki
Application of machine learning techniques to tuberculosis drug resistance analysis. Bioinform. 35(13): 2276-2282 (2019) - [j27]Yang Yang, Timothy M. Walker
DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis. Bioinform. 35(18): 3240-3249 (2019) - [j26]James Malycha
Patient centred variables with univariate associations with unplanned ICU admission: a systematic review. BMC Medical Informatics Decis. Mak. 19(1): 98:1-98:9 (2019) - [j25]Davide Morelli
Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations. Sensors 19(14): 3163 (2019) - [j24]Tingting Zhu
Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalizing Care. IEEE J. Biomed. Health Informatics 23(1): 47-58 (2019) - [j23]Glen Wright Colopy
Gaussian Processes for Personalized Interpretable Volatility Metrics in the Step-Down Ward. IEEE J. Biomed. Health Informatics 23(3): 949-959 (2019) - [c39]Girmaw Abebe Tadesse, Tingting Zhu
Cardiovascular disease diagnosis using cross-domain transfer learning. EMBC 2019: 4262-4265 - [i3]Pulkit Sharma, Farah E. Shamout, David A. Clifton:
Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality. CoRR abs/1912.00354 (2019) - [i2]Girmaw Abebe Tadesse, Tingting Zhu
Severity Detection Tool for Patients with Infectious Disease. CoRR abs/1912.05345 (2019) - 2018
- [j22]Yang Yang, Katherine E. Niehaus, Timothy M. Walker
Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinform. 34(10): 1666-1671 (2018) - [j21]Stijn Luca
Point process models for novelty detection on spatial point patterns and their extremes. Comput. Stat. Data Anal. 125: 86-103 (2018) - [j20]Delaram Jarchi
Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions. Sensors 18(11): 3705 (2018) - [j19]Drew A. Birrenkott
A Robust Fusion Model for Estimating Respiratory Rate From Photoplethysmography and Electrocardiography. IEEE Trans. Biomed. Eng. 65(9): 2033-2041 (2018) - [j18]Glen Wright Colopy
Bayesian Optimization of Personalized Models for Patient Vital-Sign Monitoring. IEEE J. Biomed. Health Informatics 22(2): 301-310 (2018) - [c38]Tingting Zhu
Identifying patient-specific trajectories in haemodialysis using Bayesian Hierarchical Gaussian Processes. BHI 2018: 186-189 - [c37]Delaram Jarchi
Estimation of HRV and SpO2 from wrist-worn commercial sensors for clinical settings. BSN 2018: 144-147 - [c36]Delaram Jarchi
Visualisation of long-term ECG signals applied to post-intensive care patients. BSN 2018: 165-168 - 2017
- [j17]Farah E. Colchester
Accidental infrastructure for groundwater monitoring in Africa. Environ. Model. Softw. 91: 241-250 (2017) - [j16]Marco A. F. Pimentel
Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters. IEEE Trans. Biomed. Eng. 64(8): 1914-1923 (2017) - [c35]Glen Wright Colopy, Marco A. F. Pimentel, Stephen J. Roberts, David A. Clifton:
Bayesian optimisation of Gaussian processes for identifying the deteriorating patient. BHI 2017: 85-88 - [c34]Glen Wright Colopy, Tingting Zhu
Likelihood-based artefact detection in continuously-acquired patient vital signs. EMBC 2017: 2146-2149 - 2016
- [j15]Stijn Luca, David A. Clifton, Bart Vanrumste:
One-class classification of point patterns of extremes. J. Mach. Learn. Res. 17: 191:1-191:21 (2016) - [j14]Alistair E. W. Johnson, Mohammad M. Ghassemi, Shamim Nemati, Katherine E. Niehaus, David A. Clifton, Gari D. Clifford:
Machine Learning and Decision Support in Critical Care. Proc. IEEE 104(2): 444-466 (2016) - [c33]Drew A. Birrenkott
Robust estimation of respiratory rate via ECG- and PPG-derived respiratory quality indices. EMBC 2016: 676-679 - [c32]Yanting Shen, Yang Yang, Sarah Parish, Zhengming Chen, Robert Clarke, David A. Clifton:
Risk prediction for cardiovascular disease using ECG data in the China kadoorie biobank. EMBC 2016: 2419-2422 - [c31]Glen Wright Colopy, Marco A. F. Pimentel, Stephen J. Roberts, David A. Clifton:
Bayesian Gaussian processes for identifying the deteriorating patient. EMBC 2016: 5311-5314 - [c30]Yuanyuan Xue, Qi Li, Liang Zhao, Jia Jia, Ling Feng, Feng Yu
Analysis of Teens' Chronic Stress on Micro-blog. WISE (2) 2016: 121-136 - 2015
- [j13]Robert Dürichen, Marco A. F. Pimentel, Lei A. Clifton
Multitask Gaussian Processes for Multivariate Physiological Time-Series Analysis. IEEE Trans. Biomed. Eng. 62(1): 314-322 (2015) - [j12]Christina Orphanidou, Timothy Bonnici, Peter Charlton
Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring. IEEE J. Biomed. Health Informatics 19(3): 832-838 (2015) - [c29]Marzyeh Ghassemi, Marco A. F. Pimentel, Tristan Naumann, Thomas Brennan, David A. Clifton, Peter Szolovits, Mengling Feng:
A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data. AAAI 2015: 446-453 - [c28]Tingting Zhu
Bayesian fusion of algorithms for the robust estimation of respiratory rate from the photoplethysmogram. EMBC 2015: 6138-6141 - [c27]Katherine E. Niehaus, Holm H. Uhlig
Phenotypic characterisation of Crohn's disease severity. EMBC 2015: 7023-7026 - [i1]Tingting Zhu
Fusing Continuous-valued Medical Labels using a Bayesian Model. CoRR abs/1503.06619 (2015) - 2014
- [j11]Marco A. F. Pimentel
A review of novelty detection. Signal Process. 99: 215-249 (2014) - [j10]Lei A. Clifton
Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors. IEEE J. Biomed. Health Informatics 18(3): 722-730 (2014) - [j9]Lei A. Clifton
Probabilistic Novelty Detection With Support Vector Machines. IEEE Trans. Reliab. 63(2): 455-467 (2014) - [j8]David A. Clifton, Lei A. Clifton
Extending the Generalised Pareto Distribution for Novelty Detection in High-Dimensional Spaces. J. Signal Process. Syst. 74(3): 323-339 (2014) - [c26]Robert Dürichen, Marco A. F. Pimentel, Lei A. Clifton, Achim Schweikard, David A. Clifton:
Multi-task Gaussian process models for biomedical applications. BHI 2014: 492-495 - [c25]Katherine E. Niehaus, Timothy M. Walker
Machine learning for the prediction of antibacterial susceptibility in Mycobacterium tuberculosis. BHI 2014: 618-621 - [c24]Alistair E. W. Johnson, Jonathan Burgess, Marco A. F. Pimentel, David A. Clifton, J. Duncan Young
Physiological trajectory of patients pre and post ICU discharge1. EMBC 2014: 3160-3163 - [c23]Yuanyuan Xue, Qi Li, Li Jin, Ling Feng, David A. Clifton, Gari D. Clifford:
Detecting Adolescent Psychological Pressures from Micro-Blog. HIS 2014: 83-94 - [c22]Robert Dürichen, Tobias Wissel, Floris Ernst
A unified approach for respiratory motion prediction and correlation with multi-task Gaussian Processes. MLSP 2014: 1-6 - 2013
- [j7]David A. Clifton, Lei A. Clifton
An Extreme Function Theory for Novelty Detection. IEEE J. Sel. Top. Signal Process. 7(1): 28-37 (2013) - [j6]Marco A. F. Pimentel, David A. Clifton, Lei A. Clifton
Modelling physiological deterioration in post-operative patient vital-sign data. Medical Biol. Eng. Comput. 51(8): 869-877 (2013) - [j5]Lei A. Clifton
Gaussian Processes for Personalized e-Health Monitoring With Wearable Sensors. IEEE Trans. Biomed. Eng. 60(1): 193-197 (2013) - [j4]David A. Clifton, David Wong
A Large-Scale Clinical Validation of an Integrated Monitoring System in the Emergency Department. IEEE J. Biomed. Health Informatics 17(4): 835-842 (2013) - [c21]Marco A. F. Pimentel, David A. Clifton, Lei A. Clifton, Lionel Tarassenko:
Probabilistic estimation of respiratory rate using Gaussian processes. EMBC 2013: 2902-2905 - [c20]Sara Khalid
A Bayesian Patient-Based Model for Detecting Deterioration in Vital Signs Using Manual Observations. FHIES 2013: 146-158 - [c19]Mauro D. Santos, David A. Clifton, Lionel Tarassenko:
Performance of Early Warning Scoring Systems to Detect Patient Deterioration in the Emergency Department. FHIES 2013: 159-169 - [c18]Yuanyuan Xue, Qi Li, Ling Feng, Gari D. Clifford, David A. Clifton:
Towards a micro-blog platform for sensing and easing adolescent psychological pressures. UbiComp (Adjunct Publication) 2013: 215-218 - [c17]Marco A. F. Pimentel, David A. Clifton, Lionel Tarassenko:
Gaussian process clustering for the functional characterisation of vital-sign trajectories. MLSP 2013: 1-6 - 2012
- [j3]Hujun Yin, David A. Clifton:
Introduction. Int. J. Neural Syst. 22(5) (2012) - [j2]Sara Khalid
A Two-Class Approach to the Detection of Physiological Deterioration in Patient Vital Signs, With Clinical Label Refinement. IEEE Trans. Inf. Technol. Biomed. 16(6): 1231-1238 (2012) - [c16]David Wong
Probabilistic detection of vital sign abnormality with Gaussian process regression. BIBE 2012: 187-192 - [c15]Marco A. F. Pimentel, David A. Clifton, Lei A. Clifton, Peter J. Watkinson, Lionel Tarassenko:
Vital-sign Data Fusion Models for Post-operative Patients. BIOSIGNALS 2012: 410-413 - [c14]Lei A. Clifton, David A. Clifton, Marco A. F. Pimentel, Peter J. Watkinson
Gaussian process regression in vital-sign early warning systems. EMBC 2012: 6161-6164 - [c13]David A. Clifton, Jeremy Gibbons
Machine learning and software engineering in health informatics. RAISE@ICSE 2012: 37-41 - 2011
- [j1]David A. Clifton, Samuel Hugueny, Lionel Tarassenko:
Novelty Detection with Multivariate Extreme Value Statistics. J. Signal Process. Syst. 65(3): 371-389 (2011) - [c12]Sara Khalid, David A. Clifton, Lei A. Clifton, Lionel Tarassenko:
Optimising Classifiers for the Detection of Physiological Deterioration in Patient Vital-sign Data. BIOSIGNALS 2011: 425-428 - [c11]Lei A. Clifton, David A. Clifton, Peter J. Watkinson, Lionel Tarassenko:
Identification of Patient Deterioration in Vital-Sign Data using One-Class Support Vector Machines. FedCSIS 2011: 125-131 - [c10]David A. Clifton, David Wong
Novelty Detection for Identifying Deterioration in Emergency Department Patients. IDEAL 2011: 220-227 - [c9]Kiryl Chykeyuk, David A. Clifton, J. Alison Noble
Feature extraction and wall motion classification of 2D stress echocardiography with relevance vector machines. ISBI 2011: 677-680 - [c8]Kiryl Chykeyuk, David A. Clifton, J. Alison Noble
Feature extraction and wall motion classification of 2D stress echocardiography with support vector machines. Medical Imaging: Computer-Aided Diagnosis 2011: 79630H - [c7]David A. Clifton, Samuel Hugueny, Lionel Tarassenko:
Pinning the tail on the distribution: A multivariate extension to the generalised Pareto distribution. MLSP 2011: 1-6 - 2010
- [c6]Samuel Hugueny, David A. Clifton, Lionel Tarassenko:
Probabilistic Patient Monitoring using Extreme Value Theory - A Multivariate, Multimodal Methodology for Detecting Patient Deterioration. BIOSIGNALS 2010: 5-12 - [c5]Samuel Hugueny, David A. Clifton, Lionel Tarassenko:
Probabilistic Patient Monitoring with Multivariate, Multimodal Extreme Value Theory. BIOSTEC (Selected Papers) 2010: 199-211
2000 – 2009
- 2008
- [p1]David A. Clifton, Lei A. Clifton
Automated Novelty Detection in Industrial Systems. Advances of Computational Intelligence in Industrial Systems 2008: 269-296 - 2007
- [c4]Lei A. Clifton, Hujun Yin
Combined Support Vector Novelty Detection for Multi-channel Combustion Data. ICNSC 2007: 495-500 - [c3]David A. Clifton, Peter R. Bannister, Lionel Tarassenko:
Novelty Detection in Large-Vehicle Turbocharger Operation. IEA/AIE 2007: 591-600 - 2006
- [c2]David A. Clifton, Peter R. Bannister, Lionel Tarassenko:
Application of an Intuitive Novelty Metric for Jet Engine Condition Monitoring. IEA/AIE 2006: 1149-1158 - [c1]David A. Clifton, Peter R. Bannister, Lionel Tarassenko:
Learning Shape for Jet Engine Novelty Detection. ISNN (2) 2006: 828-835
Coauthor Index
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