Professor Honghan Wu

  • Professor of Health Informatics and AI (Public Health)

Biography

I am a Professor of Health Informatics and AI at the School of Health and Wellbeing, University of Glasgow and an honorary professor at Hong Kong University. Before my current position, I was an associate professor at the Institute of Health Informatics (2020 May - 2024 April), UCL. I current hold an honorary associate professor position at UCL, continuing my research projects and supervisions. I am a former (2020-2023) Turing Fellow of The Alan Turing Institute and a Rutherford Fellow (2018-2022) of the Health Data Research UK. I got my BEng and PhD degrees from Southeast University, China. I worked in the industry for about six years primarily as a software developer before my PhD study.

My research lab website is at https://knowlab.github.io/, I also co-lead the Edinburgh Clinical Natural Language Processing group: https://www.ed.ac.uk/usher/clinical-natural-language-processing and co-organise the Turing Health Equity group: https://www.turing.ac.uk/research/interest-groups/health-equity.

Research interests

Machine learning, natural language processing, knowledge graph and their applications in medicine. Details of my research and team updates can be found at https://knowlab.github.io/.

Publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022 | 2021
Number of items: 46.

2024

Ji, S., Li, X., Sun, W., Dong, H., Taalas, A., Zhang, Y., Wu, H. , Pitkänen, E. and Marttinen, P. (2024) A unified review of deep learning for automated medical coding. ACM Computing Surveys, 56(12), 306. (doi: 10.1145/3664615)

Greene, C., Blackbourn, L., McGurnaghan, S., Mercer, S., Smith, D., Wild, S., Wu, H. , Jackson, C. and Scottish Diabetes Research Network Epidemiology Group, (2024) Antidepressant and antipsychotic prescribing in patients with type 2 diabetes in Scotland: a time-trend analysis from 2004-2021. British Journal of Clinical Pharmacology, (doi: 10.1111/bcp.16171) (PMID:38981672) (Early Online Publication)

Wu, J., Kim, Y. and Wu, H. (2024) Hallucination benchmark in medical visual question answering. arXiv, (doi: 10.48550/arXiv.2401.05827)

Francis, F., Luz, S., Wu, H. , Stock, S. J. and Townsend, R. (2024) Machine learning on cardiotocography data to classify fetal outcomes: a scoping review. Computers in Biology and Medicine, 172, 108220. (doi: 10.1016/j.compbiomed.2024.108220) (PMID:38489990)

Feng, W., Wu, H. , Ma, H., Tao, Z., Xu, M., Zhang, X., Lu, S., Wan, C. and Liu, Y. (2024) Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study. Journal of the American Medical Informatics Association, 31(2), pp. 445-455. (doi: 10.1093/jamia/ocad228) (PMID:38062850) (PMCID:PMC10797279)

2023

Fu, Y., Zhang, G., Lu, X., Wu, H. and Zhang, D. (2023) RMCA U-net: Hard exudates segmentation for retinal fundus images. Expert Systems with Applications, 234, 120987. (doi: 10.1016/j.eswa.2023.120987)

Groves, E., Wang, M., Abdulle, Y., Kunz, H., Hoelscher-Obermaier, J., Wu, R. and Wu, H. (2023) Benchmarking and analyzing in-context learning, fine-tuning and supervised learning for biomedical knowledge curation: a focused study on chemical entities of biological interest. arXiv, (doi: 10.48550/arXiv.2312.12989)

Wu, J., Kim, Y., Keller, E. C., Chow, J., Levine, A. P., Pontikos, N., Ibrahim, Z., Taylor, P., Williams, M. C. and Wu, H. (2023) Exploring multimodal large language models for radiology report error-checking. arXiv, (doi: 10.48550/arXiv.2312.13103)

Guellil, I. et al. (2023) Natural language processing for detecting adverse drug events: a systematic review protocol. [Protocols]

Francis, F., Luz, S., Wu, H. , Townsend, R. and Stock, S. S. (2023) Machine Learning to Classify Cardiotocography for Fetal Hypoxia Detection. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 24-27 July 2023, ISBN 9798350324471 (doi: 10.1109/embc40787.2023.10340803)

Groza, T., Wu, H. , Dinger, M. E., Danis, D., Hilton, C., Bagley, A., Davids, J. R., Luo, L., Lu, Z. and Robinson, P. N. (2023) Term-BLAST-like alignment tool for concept recognition in noisy clinical texts. Bioinformatics, 39(12), btad716. (doi: 10.1093/bioinformatics/btad716) (PMID:38001031) (PMCID:PMC10710372)

Zhang, H., Casey, A., Guellil, I., Suárez-Paniagua, V., MacRae, C., Marwick, C., Wu, H. , Guthrie, B. and Alex, B. (2023) FLAP: a framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database. Frontiers in Digital Health, 5, 1186208. (doi: 10.3389/fdgth.2023.1186208) (PMID:38090654) (PMCID:PMC10715280)

Thygesen, J. H. et al. (2023) A nationwide study of 331 rare diseases among 58 million individuals: prevalence, demographics, and COVID-19 outcomes. medRxiv, (doi: 10.1101/2023.10.12.23296948)

Casey, A. et al. (2023) Understanding the performance and reliability of NLP tools: a comparison of four NLP tools predicting stroke phenotypes in radiology reports. Frontiers in Digital Health, 5, 1184919. (doi: 10.3389/fdgth.2023.1184919) (PMID:37840686) (PMCID:PMC10569314)

Alsaleh, M. M., Allery, F., Choi, J. W., JW, C., Hama, T., McQuillin, A., Wu, H. and Thygesen, J. H. (2023) Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review. International Journal of Medical Informatics, 175, 105088. (doi: 10.1016/j.ijmedinf.2023.105088) (PMID:37156169)

Wang, M., Kloczko, E., Altayeb, A., Farrugia, M., Gupta, G., Wu, H. and Hirani, N. (2023) Towards automated dermatology triage: deep learning and knowledge-driven approaches. Research Square, (doi: 10.21203/rs.3.rs-2889033/v1)

Greene, C. R.L., Ward-Penny, H., Ioannou, M. F., Wild, S. H., Wu, H. , Smith, D. J. and Jackson, C. A. (2023) Antidepressant and antipsychotic drug prescribing and diabetes outcomes: A systematic review of observational studies. Diabetes Research and Clinical Practice, 199, 110649. (doi: 10.1016/j.diabres.2023.110649) (PMID:37004975)

Dong, H., Suárez‑Paniagua, V., Zhang, H., Wang, M., Casey, A., Davidson, E., Chen, J., Alex, B., Whiteley, W. and Wu, H. (2023) Ontology-driven and weakly supervised rare disease identification from clinical notes. BMC Medical Informatics and Decision Making, 23, 86. (doi: 10.1186/s12911-023-02181-9) (PMID:37147628) (PMCID:PMC10162001)

Davidson, E. M. et al. (2023) The epidemiological characteristics of stroke phenotypes defined with ICD-10 and free-text: a cohort study linked to electronic health records. MedRxiv, (doi: 10.1101/2023.04.03.23288096)

Kuan, V. et al. (2023) Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study. Lancet Digital Health, 5(1), e16-e27. (doi: 10.1016/S2589-7500(22)00187-X) (PMID:36460578)

Ibrahim, Z., Wu, H. and Wiratunga, N. (2023) Preface: The 6th International Workshop on Knowledge Discovery in Healthcare Data (KDH). In: KDH@IJCAI 2023 Knowledge Discovery from Healthcare Data 2023, Macao, China, 20 Aug 2023,

Wu, J., Shi, D., Hasan, A. and Wu, H. (2023) KnowLab at RadSum23: Comparing Pre-trained Language Models in Radiology Report Summarization. In: The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, Toronto, Canada, July 2023, pp. 535-540. ISBN 9781959429852 (doi: 10.18653/v1/2023.bionlp-1.54)

2022

Wu, H. et al. (2022) A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. npj Digital Medicine, 5, 186. (doi: 10.1038/s41746-022-00730-6) (PMID:36544046) (PMCID:PMC9770568)

Dong, H., Falis, M., Whiteley, W., Alex, B., Matteson, J., Ji, S., Chen, J. and Wu, H. (2022) Automated clinical coding: what, why, and where we are? npj Digital Medicine, 5, 159. (doi: 10.1038/s41746-022-00705-7) (PMID:36273236) (PMCID:PMC9588058)

Guellil, I., Wu, J., Wu, H. , Sun, T. and Alex, B. (2022) Edinburgh_UCL_Health@SMM4H'22: From Glove to Flair for Handling Imbalanced Healthcare Corpora Related to Adverse Drug Events, Change in Medication and Self-reporting Vaccination. In: Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Republic of Korea, 12-17 Oct 2022, pp. 148-152.

Chen, Q. et al. (2022) Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations. Database, 2022, baac069. (doi: 10.1093/database/baac069) (PMID:36043400) (PMCID:PMC9428574)

Wu, J., Smith, R. and Wu, H. (2022) Adverse Childhood Experiences identification from clinical notes with ontologies and NLP. arXiv, (doi: 10.48550/arXiv.2208.11466)

Wu, J., Smith, R. and Wu, H. (2022) Ontology-driven self-supervision for adverse childhood experiences identification using social media datasets. arXiv, (doi: 10.48550/arXiv.2208.11701)

Cheung, J. P. Y., Kuang, X., Lai, M. K. L., Cheung, K. M.‑C., Karppinen, J., Samartzis, D., Wu, H. , Zhao, F., Zheng, Z. and Zhang, T. (2022) Learning-based fully automated prediction of lumbar disc degeneration progression with specified clinical parameters and preliminary validation. European Spine Journal, 31(8), pp. 1960-1968. (doi: 10.1007/s00586-021-07020-x) (PMID:34657211)

Wan, C., Read, S., Wu, H. , Lu, S., Zhang, X., Wild, S. H. and Liu, Y. (2022) Prediction of five-year cardiovascular disease risk in people with type 2 diabetes mellitus: derivation in Nanjing, China and external validation in Scotland, UK. Global Heart, 17(1), 46. (doi: 10.5334/gh.1131) (PMID:36051323) (PMCID:PMC9336685)

Kuang, X. et al. (2022) Spine-GFlow: A hybrid learning framework for robust multi-tissue segmentation in lumbar MRI without manual annotation. Computerized Medical Imaging and Graphics, 99, 102091. (doi: 10.1016/j.compmedimag.2022.102091)

Thygesen, J. H. et al. (2022) COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records. Lancet Digital Health, 7(4), e542-e557. (doi: 10.1016/S2589-7500(22)00091-7) (PMID:35690576) (PMCID:PMC9179175)

Wu, H. , Sylolypavan, A., Wang, M. and Wild, S. (2022) Quantifying Health Inequalities Induced by Data and AI Models. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, Austria, 23-29 July 2022, ISBN 9781956792003

Straw, I. and Wu, H. (2022) Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction. BMJ Health Care Inform, 29, e100457. (doi: 10.1136/bmjhci-2021-100457) (PMID:35470133) (PMCID:PMC9039354)

Zhang, H., Thygesen, J. H., Shi, T., Gkoutos, G. V., Hemingway, H., Guthrie, B., Wu, H. and Genomics England Research Consortium, (2022) Increased COVID-19 mortality rate in rare disease patients: a retrospective cohort study in participants of the Genomics England 100,000 Genomes project. Orphanet Journal of Rare Diseases, 17, 166. (doi: 10.1186/s13023-022-02312-x) (PMID:35414031) (PMCID:PMC9003178)

Ibrahim, Z.M. et al. (2022) A knowledge distillation ensemble framework for predicting short- and long-term hospitalization outcomes from electronic health records data. IEEE Journal of Biomedical and Health Informatics, 26(1), pp. 423-435. (doi: 10.1109/JBHI.2021.3089287) (PMID:34129509)

Francis, F., Wu, H. , Luz, S., Townsend, R. and Stock, S. (2022) Detecting Intrapartum Fetal Hypoxia from Cardiotocography Using Machine Learning. In: 49th Computing in Cardiology Conference CinC 2022, Tampere, Finland, 4-7 Sept 2022, ISBN 9798350300970 (doi: 10.22489/CinC.2022.339)

Wang, M., Francis, F., Kunz, H., Zhang, X., Wan, C., Liu, Y., Taylor, P., Wild, S. H. and Wu, H. (2022) Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: A systematic review. Intelligence-Based Medicine, 6, 100072. (doi: 10.1016/j.ibmed.2022.100072)

2021

Zhang, H., Thygesen, J. and Wu, H. (2021) Increased COVID-19 related mortality rate for patients with rare diseases: a retrospective cohort study with data from Genomics England. Lancet, 398(Sup 2), S95. (PMCID:PMC8617313)

Fairfield, C. J. et al. (2021) ToKSA - Tokenized Key Sentence Annotation - a novel method for rapid approximation of ground truth for natural language processing. medRxiv, (doi: 10.1101/2021.10.06.21264629)

Davidson, E. M. et al. (2021) The reporting quality of natural language processing studies: systematic review of studies of radiology reports. BMC Medical Imaging, 21, 142. (doi: 10.1186/s12880-021-00671-8) (PMID:34600486) (PMCID:PMC8487512)

Rannikmäe, R., Wu, H. , Tominey, S., Whiteley, W., Allen, N., Sudlow, C. and UK Biobank, (2021) Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke. BMC Medical Informatics and Decision Making, 21, 191. (doi: 10.1186/s12911-021-01556-0) (PMID:34130677) (PMCID:PMC8204419)

Casey, A. et al. (2021) A systematic review of natural language processing applied to radiology reports. BMC Medical Informatics and Decision Making, 21, 179. (doi: 10.1186/s12911-021-01533-7) (PMID:34082729) (PMCID:PMC8176715)

Zhang, H., Ferguson, A., Robertson, G., Jiang, M., Zhang, T., Sudlow, C., Smith, K., Rannikmae, K. and Wu, H. (2021) Benchmarking network-based gene prioritization methods for cerebral small vessel disease. Briefings in Bioinformatics, 22(5), bbab006. (doi: 10.1093/bib/bbab006) (PMID:33634312) (PMCID:PMC8425308)

Dong, H., Suárez-Paniagua, V., Zhang, H., Wang, M., Whitfield, E. and Wu, H. (2021) Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision. In: 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Online, 31 Oct--4 Nov 2021, pp. 2294-2298. ISBN 9781728111797 (doi: 10.1109/embc46164.2021.9630043)

Mirza, L. et al. (2021) Investigating the association between physical health comorbidities and disability in individuals with severe mental illness. European Psychiatry, 64(1), e77. (doi: 10.1192/j.eurpsy.2021.2255) (PMID:34842128) (PMCID:PMC8727716)

This list was generated on Thu Nov 21 04:44:28 2024 GMT.
Jump to: Articles | Conference Proceedings | Protocols
Number of items: 46.

Articles

Ji, S., Li, X., Sun, W., Dong, H., Taalas, A., Zhang, Y., Wu, H. , Pitkänen, E. and Marttinen, P. (2024) A unified review of deep learning for automated medical coding. ACM Computing Surveys, 56(12), 306. (doi: 10.1145/3664615)

Greene, C., Blackbourn, L., McGurnaghan, S., Mercer, S., Smith, D., Wild, S., Wu, H. , Jackson, C. and Scottish Diabetes Research Network Epidemiology Group, (2024) Antidepressant and antipsychotic prescribing in patients with type 2 diabetes in Scotland: a time-trend analysis from 2004-2021. British Journal of Clinical Pharmacology, (doi: 10.1111/bcp.16171) (PMID:38981672) (Early Online Publication)

Wu, J., Kim, Y. and Wu, H. (2024) Hallucination benchmark in medical visual question answering. arXiv, (doi: 10.48550/arXiv.2401.05827)

Francis, F., Luz, S., Wu, H. , Stock, S. J. and Townsend, R. (2024) Machine learning on cardiotocography data to classify fetal outcomes: a scoping review. Computers in Biology and Medicine, 172, 108220. (doi: 10.1016/j.compbiomed.2024.108220) (PMID:38489990)

Feng, W., Wu, H. , Ma, H., Tao, Z., Xu, M., Zhang, X., Lu, S., Wan, C. and Liu, Y. (2024) Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study. Journal of the American Medical Informatics Association, 31(2), pp. 445-455. (doi: 10.1093/jamia/ocad228) (PMID:38062850) (PMCID:PMC10797279)

Fu, Y., Zhang, G., Lu, X., Wu, H. and Zhang, D. (2023) RMCA U-net: Hard exudates segmentation for retinal fundus images. Expert Systems with Applications, 234, 120987. (doi: 10.1016/j.eswa.2023.120987)

Groves, E., Wang, M., Abdulle, Y., Kunz, H., Hoelscher-Obermaier, J., Wu, R. and Wu, H. (2023) Benchmarking and analyzing in-context learning, fine-tuning and supervised learning for biomedical knowledge curation: a focused study on chemical entities of biological interest. arXiv, (doi: 10.48550/arXiv.2312.12989)

Wu, J., Kim, Y., Keller, E. C., Chow, J., Levine, A. P., Pontikos, N., Ibrahim, Z., Taylor, P., Williams, M. C. and Wu, H. (2023) Exploring multimodal large language models for radiology report error-checking. arXiv, (doi: 10.48550/arXiv.2312.13103)

Groza, T., Wu, H. , Dinger, M. E., Danis, D., Hilton, C., Bagley, A., Davids, J. R., Luo, L., Lu, Z. and Robinson, P. N. (2023) Term-BLAST-like alignment tool for concept recognition in noisy clinical texts. Bioinformatics, 39(12), btad716. (doi: 10.1093/bioinformatics/btad716) (PMID:38001031) (PMCID:PMC10710372)

Zhang, H., Casey, A., Guellil, I., Suárez-Paniagua, V., MacRae, C., Marwick, C., Wu, H. , Guthrie, B. and Alex, B. (2023) FLAP: a framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database. Frontiers in Digital Health, 5, 1186208. (doi: 10.3389/fdgth.2023.1186208) (PMID:38090654) (PMCID:PMC10715280)

Thygesen, J. H. et al. (2023) A nationwide study of 331 rare diseases among 58 million individuals: prevalence, demographics, and COVID-19 outcomes. medRxiv, (doi: 10.1101/2023.10.12.23296948)

Casey, A. et al. (2023) Understanding the performance and reliability of NLP tools: a comparison of four NLP tools predicting stroke phenotypes in radiology reports. Frontiers in Digital Health, 5, 1184919. (doi: 10.3389/fdgth.2023.1184919) (PMID:37840686) (PMCID:PMC10569314)

Alsaleh, M. M., Allery, F., Choi, J. W., JW, C., Hama, T., McQuillin, A., Wu, H. and Thygesen, J. H. (2023) Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review. International Journal of Medical Informatics, 175, 105088. (doi: 10.1016/j.ijmedinf.2023.105088) (PMID:37156169)

Wang, M., Kloczko, E., Altayeb, A., Farrugia, M., Gupta, G., Wu, H. and Hirani, N. (2023) Towards automated dermatology triage: deep learning and knowledge-driven approaches. Research Square, (doi: 10.21203/rs.3.rs-2889033/v1)

Greene, C. R.L., Ward-Penny, H., Ioannou, M. F., Wild, S. H., Wu, H. , Smith, D. J. and Jackson, C. A. (2023) Antidepressant and antipsychotic drug prescribing and diabetes outcomes: A systematic review of observational studies. Diabetes Research and Clinical Practice, 199, 110649. (doi: 10.1016/j.diabres.2023.110649) (PMID:37004975)

Dong, H., Suárez‑Paniagua, V., Zhang, H., Wang, M., Casey, A., Davidson, E., Chen, J., Alex, B., Whiteley, W. and Wu, H. (2023) Ontology-driven and weakly supervised rare disease identification from clinical notes. BMC Medical Informatics and Decision Making, 23, 86. (doi: 10.1186/s12911-023-02181-9) (PMID:37147628) (PMCID:PMC10162001)

Davidson, E. M. et al. (2023) The epidemiological characteristics of stroke phenotypes defined with ICD-10 and free-text: a cohort study linked to electronic health records. MedRxiv, (doi: 10.1101/2023.04.03.23288096)

Kuan, V. et al. (2023) Identifying and visualising multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study. Lancet Digital Health, 5(1), e16-e27. (doi: 10.1016/S2589-7500(22)00187-X) (PMID:36460578)

Wu, H. et al. (2022) A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. npj Digital Medicine, 5, 186. (doi: 10.1038/s41746-022-00730-6) (PMID:36544046) (PMCID:PMC9770568)

Dong, H., Falis, M., Whiteley, W., Alex, B., Matteson, J., Ji, S., Chen, J. and Wu, H. (2022) Automated clinical coding: what, why, and where we are? npj Digital Medicine, 5, 159. (doi: 10.1038/s41746-022-00705-7) (PMID:36273236) (PMCID:PMC9588058)

Chen, Q. et al. (2022) Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations. Database, 2022, baac069. (doi: 10.1093/database/baac069) (PMID:36043400) (PMCID:PMC9428574)

Wu, J., Smith, R. and Wu, H. (2022) Adverse Childhood Experiences identification from clinical notes with ontologies and NLP. arXiv, (doi: 10.48550/arXiv.2208.11466)

Wu, J., Smith, R. and Wu, H. (2022) Ontology-driven self-supervision for adverse childhood experiences identification using social media datasets. arXiv, (doi: 10.48550/arXiv.2208.11701)

Cheung, J. P. Y., Kuang, X., Lai, M. K. L., Cheung, K. M.‑C., Karppinen, J., Samartzis, D., Wu, H. , Zhao, F., Zheng, Z. and Zhang, T. (2022) Learning-based fully automated prediction of lumbar disc degeneration progression with specified clinical parameters and preliminary validation. European Spine Journal, 31(8), pp. 1960-1968. (doi: 10.1007/s00586-021-07020-x) (PMID:34657211)

Wan, C., Read, S., Wu, H. , Lu, S., Zhang, X., Wild, S. H. and Liu, Y. (2022) Prediction of five-year cardiovascular disease risk in people with type 2 diabetes mellitus: derivation in Nanjing, China and external validation in Scotland, UK. Global Heart, 17(1), 46. (doi: 10.5334/gh.1131) (PMID:36051323) (PMCID:PMC9336685)

Kuang, X. et al. (2022) Spine-GFlow: A hybrid learning framework for robust multi-tissue segmentation in lumbar MRI without manual annotation. Computerized Medical Imaging and Graphics, 99, 102091. (doi: 10.1016/j.compmedimag.2022.102091)

Thygesen, J. H. et al. (2022) COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records. Lancet Digital Health, 7(4), e542-e557. (doi: 10.1016/S2589-7500(22)00091-7) (PMID:35690576) (PMCID:PMC9179175)

Straw, I. and Wu, H. (2022) Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction. BMJ Health Care Inform, 29, e100457. (doi: 10.1136/bmjhci-2021-100457) (PMID:35470133) (PMCID:PMC9039354)

Zhang, H., Thygesen, J. H., Shi, T., Gkoutos, G. V., Hemingway, H., Guthrie, B., Wu, H. and Genomics England Research Consortium, (2022) Increased COVID-19 mortality rate in rare disease patients: a retrospective cohort study in participants of the Genomics England 100,000 Genomes project. Orphanet Journal of Rare Diseases, 17, 166. (doi: 10.1186/s13023-022-02312-x) (PMID:35414031) (PMCID:PMC9003178)

Ibrahim, Z.M. et al. (2022) A knowledge distillation ensemble framework for predicting short- and long-term hospitalization outcomes from electronic health records data. IEEE Journal of Biomedical and Health Informatics, 26(1), pp. 423-435. (doi: 10.1109/JBHI.2021.3089287) (PMID:34129509)

Wang, M., Francis, F., Kunz, H., Zhang, X., Wan, C., Liu, Y., Taylor, P., Wild, S. H. and Wu, H. (2022) Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: A systematic review. Intelligence-Based Medicine, 6, 100072. (doi: 10.1016/j.ibmed.2022.100072)

Zhang, H., Thygesen, J. and Wu, H. (2021) Increased COVID-19 related mortality rate for patients with rare diseases: a retrospective cohort study with data from Genomics England. Lancet, 398(Sup 2), S95. (PMCID:PMC8617313)

Fairfield, C. J. et al. (2021) ToKSA - Tokenized Key Sentence Annotation - a novel method for rapid approximation of ground truth for natural language processing. medRxiv, (doi: 10.1101/2021.10.06.21264629)

Davidson, E. M. et al. (2021) The reporting quality of natural language processing studies: systematic review of studies of radiology reports. BMC Medical Imaging, 21, 142. (doi: 10.1186/s12880-021-00671-8) (PMID:34600486) (PMCID:PMC8487512)

Rannikmäe, R., Wu, H. , Tominey, S., Whiteley, W., Allen, N., Sudlow, C. and UK Biobank, (2021) Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke. BMC Medical Informatics and Decision Making, 21, 191. (doi: 10.1186/s12911-021-01556-0) (PMID:34130677) (PMCID:PMC8204419)

Casey, A. et al. (2021) A systematic review of natural language processing applied to radiology reports. BMC Medical Informatics and Decision Making, 21, 179. (doi: 10.1186/s12911-021-01533-7) (PMID:34082729) (PMCID:PMC8176715)

Zhang, H., Ferguson, A., Robertson, G., Jiang, M., Zhang, T., Sudlow, C., Smith, K., Rannikmae, K. and Wu, H. (2021) Benchmarking network-based gene prioritization methods for cerebral small vessel disease. Briefings in Bioinformatics, 22(5), bbab006. (doi: 10.1093/bib/bbab006) (PMID:33634312) (PMCID:PMC8425308)

Mirza, L. et al. (2021) Investigating the association between physical health comorbidities and disability in individuals with severe mental illness. European Psychiatry, 64(1), e77. (doi: 10.1192/j.eurpsy.2021.2255) (PMID:34842128) (PMCID:PMC8727716)

Conference Proceedings

Francis, F., Luz, S., Wu, H. , Townsend, R. and Stock, S. S. (2023) Machine Learning to Classify Cardiotocography for Fetal Hypoxia Detection. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 24-27 July 2023, ISBN 9798350324471 (doi: 10.1109/embc40787.2023.10340803)

Ibrahim, Z., Wu, H. and Wiratunga, N. (2023) Preface: The 6th International Workshop on Knowledge Discovery in Healthcare Data (KDH). In: KDH@IJCAI 2023 Knowledge Discovery from Healthcare Data 2023, Macao, China, 20 Aug 2023,

Wu, J., Shi, D., Hasan, A. and Wu, H. (2023) KnowLab at RadSum23: Comparing Pre-trained Language Models in Radiology Report Summarization. In: The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, Toronto, Canada, July 2023, pp. 535-540. ISBN 9781959429852 (doi: 10.18653/v1/2023.bionlp-1.54)

Guellil, I., Wu, J., Wu, H. , Sun, T. and Alex, B. (2022) Edinburgh_UCL_Health@SMM4H'22: From Glove to Flair for Handling Imbalanced Healthcare Corpora Related to Adverse Drug Events, Change in Medication and Self-reporting Vaccination. In: Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Republic of Korea, 12-17 Oct 2022, pp. 148-152.

Wu, H. , Sylolypavan, A., Wang, M. and Wild, S. (2022) Quantifying Health Inequalities Induced by Data and AI Models. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, Austria, 23-29 July 2022, ISBN 9781956792003

Francis, F., Wu, H. , Luz, S., Townsend, R. and Stock, S. (2022) Detecting Intrapartum Fetal Hypoxia from Cardiotocography Using Machine Learning. In: 49th Computing in Cardiology Conference CinC 2022, Tampere, Finland, 4-7 Sept 2022, ISBN 9798350300970 (doi: 10.22489/CinC.2022.339)

Dong, H., Suárez-Paniagua, V., Zhang, H., Wang, M., Whitfield, E. and Wu, H. (2021) Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision. In: 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Online, 31 Oct--4 Nov 2021, pp. 2294-2298. ISBN 9781728111797 (doi: 10.1109/embc46164.2021.9630043)

Protocols

Guellil, I. et al. (2023) Natural language processing for detecting adverse drug events: a systematic review protocol. [Protocols]

This list was generated on Thu Nov 21 04:44:28 2024 GMT.

Prior publications

ORCiD

Ibrahim, Z., Wu, H., Wiratunga, N., (2023) Preface: The 6th International Workshop on Knowledge Discovery in Healthcare Data (KDH) CEUR Workshop Proceedings (eid: 2-s2.0-85173525601)(issn: 16130073); source: Scopus - Elsevier

Thygesen JH, Tomlinson C, Hollings S, Mizani M, Handy A, Akbari A, Banerjee A, Cooper J, Lai A, Li K, Mateen B, Sattar N, Sofat R, Torralbo A, Wu H, Denaxas S, (2021) Understanding COVID-19 trajectories from a nationwide linked electronic health record cohort of 56 million people: phenotypes, severity, waves & vaccination (ppr: PPR417361)(doi: 10.1101/2021.11.08.21265312); source: Europe PubMed Central

Hang Dong, Víctor Suárez-Paniagua, William Whiteley, Honghan Wu, (2021) Explainable automated coding of clinical notes using hierarchical label-wise attention networks and label embedding initialisation Journal of Biomedical Informatics (pmid: 33711543)(doi: 10.1016/j.jbi.2021.103728)(eid: 2-s2.0-85104899646)(issn: 15320464)(doi: 10.48550/arxiv.2010.15728)(eid: 2-s2.0-85181603157)(issn: 23318422); source: Honghan Wu

Wood A, Denholm R, Hollings S, Cooper J, Ip S, Walker V, Denaxas S, Akbari A, Banerjee A, Whiteley W, Lai A, Sterne J, Sudlow C, CVD-COVID-UK consortium, (2021) Linked electronic health records for research on a nationwide cohort of more than 54 million people in England: data resource. BMJ (Clinical research ed.) (pmid: 33827854)(pmc: PMC8413899)(doi: 10.1136/bmj.n826); source: Europe PubMed Central

Whitfield E, Coffey C, Zhang H, Shi T, Wu X, Li Q, Wu H, (2021) Axes of Prognosis: Identifying Subtypes of COVID-19 Outcomes (ppr: PPR302376)(doi: 10.1101/2021.03.16.21253371); source: Europe PubMed Central

Wu H, Zhang H, Karwath A, Ibrahim Z, Shi T, Zhang X, Wang K, Sun J, Dhaliwal K, Bean D, Cardoso VR, Li K, Teo JT, Banerjee A, Gao-Smith F, Guthrie B, (2021) Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2. Journal of the American Medical Informatics Association : JAMIA (pmid: 33185672)(pmc: PMC7717299)(doi: 10.1093/jamia/ocaa295); source: Europe PubMed Central

Whitfield E, Coffey C, Zhang H, Shi T, Wu X, Li Q, Wu H, (2021) Axes of Prognosis: Identifying Subtypes of COVID-19 Outcomes. AMIA ... Annual Symposium proceedings. AMIA Symposium (pmid: 35308999)(pmc: PMC8861682); source: Europe PubMed Central

Yuan Y, Sun C, Tang X, Cheng C, Mombaerts L, Wang M, Hu T, Sun C, Guo Y, Li X, Xu H, Ren T, Xiao Y, Xiao Y, Zhu H, Wu H, Li K, Chen C, Liu Y, Yan L, (2020) Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China. Engineering (Beijing, China) (pmid: 33282444)(pmc: PMC7695569)(doi: 10.1016/j.eng.2020.10.013); source: Europe PubMed Central

Kuang X, Cheung JP, Wu H, Dokos S, Zhang T, (2020) MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference (pmid: 33018308)(doi: 10.1109/embc44109.2020.9175987); source: Europe PubMed Central

Banerjee A, Chen S, Pasea L, Lai A, Katsoulis M, Denaxas S, Nafilyan V, Williams B, Wong WK, Bakhai A, Khunti K, Pillay D, Noursadeghi M, Wu H, Hemingway H, (2020) Excess deaths in people with cardiovascular diseases during the COVID-19 pandemic. (other-id: PPR174275)(doi: 10.1101/2020.06.10.20127175); source: Europe PubMed Central

Wu H, Wang M, Zeng Q, Chen W, Nind T, Jefferson E, Bennie M, Black C, Pan JZ, Sudlow C, Robertson D, (2020) Knowledge Driven Phenotyping. Studies in health technology and informatics (pmid: 32570642)(doi: 10.3233/shti200425); source: Europe PubMed Central

(2020) Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK medrxiv (doi: 10.1101/2020.04.28.20082222)(ppr: PPR342175)(doi: 10.2139/ssrn.3590468); source: Honghan Wu

Carr E, Bendayan R, Bean D, Stammers M, Wang W, Zhang H, Searle T, Searle T, Kraljevic Z, Shek A, Phan HTT, Muruet W, Shinton AJ, Shi T, Zhang X, Dobson R, (2020) Evaluation and Improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study (other-id: PPR156364)(doi: 10.1101/2020.04.24.20078006); source: Europe PubMed Central

Ibrahim ZM, Wu H, Hamoud A, Stappen L, Dobson RJB, Agarossi A, (2020) On classifying sepsis heterogeneity in the ICU: insight using machine learning. Journal of the American Medical Informatics Association : JAMIA (pmid: 31951005)(pmc: PMC7025363)(doi: 10.1093/jamia/ocz211); source: Europe PubMed Central

Ibrahim, Z.M., Bean, D., Searle, T., Qian, L., Wu, H., Shek, A., Kraljevic, Z., Galloway, J., Norton, S., Teo, J.T., Dobson, R.J.B., (2020) A knowledge distillation ensemble framework for predicting short and long-term hospitalisation outcomes from electronic health records data arXiv (doi: 10.48550/arxiv.2011.09361)(eid: 2-s2.0-85170600593)(issn: 23318422); source: Scopus - Elsevier

Bendayan, R., Wu, H., Kraljevic, Z., Stewart, R., Searle, T., Chaturvedi, J., Das-Munshi, J., Ibrahim, Z., Mascio, A., Roberts, A., Bean, D., Dobson, R., (2020) Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR arXiv (doi: 10.48550/arxiv.2002.08901)(eid: 2-s2.0-85171036145)(issn: 23318422); source: Scopus - Elsevier

Ibrahim, Z., Wu, H., Dobson, R., (2020) Modeling rare interactions in time series data through qualitative change: application to outcome prediction in intensive care units arXiv (doi: 10.48550/arxiv.2004.01431)(eid: 2-s2.0-85171028712)(issn: 23318422); source: Scopus - Elsevier

Wu H, Hodgson K, Dyson S, Morley KI, Ibrahim ZM, Iqbal E, Stewart R, Dobson RJ, Sudlow C, (2019) Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach. JMIR medical informatics (pmid: 31845899)(pmc: PMC6938594)(doi: 10.2196/14782); source: Europe PubMed Central

Kugathasan P, Wu H, Gaughran F, Nielsen RE, Pritchard M, Dobson R, Stewart R, Stubbs B, (2019) Association of physical health multimorbidity with mortality in people with schizophrenia spectrum disorders: Using a novel semantic search system that captures physical diseases in electronic patient records. Schizophrenia research (pmid: 31787481)(doi: 10.1016/j.schres.2019.10.061); source: Europe PubMed Central

Bean DM, Teo J, Wu H, Oliveira R, Patel R, Bendayan R, Shah AM, Dobson RJB, Scott PA, (2019) Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data. PloS one (pmid: 31765395)(pmc: PMC6876873)(doi: 10.1371/journal.pone.0225625); source: Europe PubMed Central

Honghan Wu, Karen Hodgson, Sue Dyson, Katherine I Morley, Zina M Ibrahim, Ehtesham Iqbal, Robert Stewart, Richard JB Dobson, Cathie Sudlow, (2019) Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach (Preprint) (doi: 10.2196/preprints.14782); source: Crossref

(2019) Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches (arxiv: arXiv:1903.03985v2)(doi: 10.48550/arxiv.1903.03985)(eid: 2-s2.0-85170726104)(issn: 23318422); source: Honghan Wu

Wu, H., Hodgson, K., Dyson, S., Morley, K.I., Ibrahim, Z.M., Iqbal, E., Stewart, R., Dobson, R.J.B., Sudlow, C., (2019) Efficiently Reusing Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: Methodology Study arXiv (doi: 10.48550/arxiv.1903.03995)(eid: 2-s2.0-85169963298)(issn: 23318422); source: Scopus - Elsevier

Honghan Wu, Giulia Toti, Katherine I Morley, Zina M Ibrahim, Amos Folarin, Richard Jackson, Ismail Kartoglu, Asha Agrawal, Clive Stringer, Darren Gale, Genevieve Gorrell, Angus Roberts, Matthew Broadbent, Robert Stewart, Richard JB Dobson, (2018) SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research* Journal of the American Medical Informatics Association (doi: 10.1093/jamia/ocx160)(issn: 1067-5027); source: Crossref Metadata Search

Bean, D.M., Wu, H., Iqbal, E., Dzahini, O., Ibrahim, Z.M., Broadbent, M., Stewart, R., Dobson, R.J.B., (2018) Erratum: Author Correction: Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records (Scientific reports (2017) 7 1 (16416)) Scientific reports (doi: 10.1038/s41598-018-22521-4)(eid: 2-s2.0-85070467011)(issn: 20452322); source: Scopus - Elsevier

Bach, K., Bunescu, R., Farri, O., Guo, A., Hasan, S., Ibrahim, Z., Marling, C., Raffa, J., Rubin, J., Wu, H., (2018) Preface: The 3rd international workshop on Knowledge Discovery in Healthcare Data (KDH) CEUR Workshop Proceedings (eid: 2-s2.0-85051034726)(issn: 16130073); source: Scopus - Elsevier

Wu H, Toti G, Morley KI, Ibrahim ZM, Folarin A, Jackson R, Kartoglu I, Agrawal A, Stringer C, Gale D, Gorrell G, Roberts A, Broadbent M, Stewart R, Dobson RJ, (2017) SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research (other-id: PPR16181)(doi: 10.1101/235622); source: Europe PubMed Central

Ehtesham Iqbal, Robbie Mallah, Daniel Rhodes, Honghan Wu, Alvin Romero, Nynn Chang, Olubanke Dzahini, Chandra Pandey, Matthew Broadbent, Robert Stewart, Richard J. B. Dobson, Zina M. Ibrahim, Tudor Groza, (2017) ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records PLOS ONE (doi: 10.1371/journal.pone.0187121)(issn: 1932-6203); source: Crossref Metadata Search

Daniel M. Bean, Honghan Wu, Olubanke Dzahini, Matthew Broadbent, Robert Stewart, Richard J. B. Dobson, (2017) Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records Scientific Reports (doi: 10.1038/s41598-017-16674-x)(issn: 2045-2322); source: Crossref Metadata Search

Honghan Wu, Giulia Toti, Katherine I Morley, Zina Ibrahim, Amos Folarin, Ismail Kartoglu, Richard Jackson, Asha Agrawal, Clive Stringer, Darren Gale, Genevieve M Gorrell, Angus Roberts, Matthew Broadbent, Robert Stewart, Richard J B Dobson, (2017) SemEHR: surfacing semantic data from clinical notes in electronic health records for tailored care, trial recruitment, and clinical research The Lancet (doi: 10.1016/s0140-6736(17)33032-5)(issn: 0140-6736); source: Crossref Metadata Search

Richard Jackson, Ismail Emre Kartoglu, Asha Agrawal, Kenneth Lui, Honghan Wu, Tudor Groza, Angus Roberts, Genevieve Gorrell, Xingyi Song, Damian Lewsley, Doug Northwood, Amos Folarin, Clive Stringer, Robert Stewart, Richard Dobson, (2017) CogStack - Experiences Of Deploying Integrated Information Retrieval And Extraction Services In A Large National Health Service Foundation Trust Hospital (doi: 10.1101/123299); source: Crossref Metadata Search

, , , , , , (2017) Automated PDF highlighting to support faster curation of literature for Parkinson's and Alzheimer's disease. Database : the journal of biological databases and curation (pmid: 28365743)(pmc: PMC5467557)(doi: 10.1093/database/bax027); source: Europe PubMed Central

Jose Manuel Gomez-Perez, Jeff Z. Pan, Guido Vetere, Honghan Wu, (2017) Enterprise Knowledge Graph: An Introduction Exploiting Linked Data and Knowledge Graphs in Large Organisations (doi: 10.1007/978-3-319-45654-6_1); source: Crossref Metadata Search

Jeff Z. Pan, Jose Manuel Gomez-Perez, Guido Vetere, Honghan Wu, Yuting Zhao, Marco Monti, (2017) Enterprise Knowledge Graph: Looking into the Future Exploiting Linked Data and Knowledge Graphs in Large Organisations (doi: 10.1007/978-3-319-45654-6_9); source: Crossref Metadata Search

Jeff Z. Pan, Guido Vetere, Jose Manuel Gomez-Perez, Honghan Wu, (2017) Exploiting Linked Data and Knowledge Graphs in Large Organisations (doi: 10.1007/978-3-319-45654-6); source: Crossref Metadata Search

Chandra Pandey, Zina Ibrahim, Honghan Wu, Ehtesham Iqbal, Richard Dobson, (2017) Improving RNN with Attention and Embedding for Adverse Drug Reactions Proceedings of the 2017 International Conference on Digital Health - DH '17 (doi: 10.1145/3079452.3079501); source: Crossref Metadata Search

Ronald Denaux, Yuan Ren, Boris Villazon-Terrazas, Panos Alexopoulos, Alessandro Faraotti, Honghan Wu, (2017) Knowledge Architecture for Organisations Exploiting Linked Data and Knowledge Graphs in Large Organisations (doi: 10.1007/978-3-319-45654-6_3); source: Crossref Metadata Search

Boris Villazon-Terrazas, Nuria Garcia-Santa, Yuan Ren, Alessandro Faraotti, Honghan Wu, Yuting Zhao, Guido Vetere, Jeff Z. Pan, (2017) Knowledge Graph Foundations Exploiting Linked Data and Knowledge Graphs in Large Organisations (doi: 10.1007/978-3-319-45654-6_2); source: Crossref Metadata Search

Ibrahim, Z., Wu, H., Bach, K., Dobson, R., Denaxas, S., Wiratunga, N., Massie, S., Sani, S., (2017) Preface: The 2nd International Workshop on Knowledge Discovery in Healthcare Data (KDH) CEUR Workshop Proceedings (eid: 2-s2.0-85029108383)(issn: 16130073); source: Scopus - Elsevier

Alessandro Moschitti, Kateryna Tymoshenko, Panos Alexopoulos, Andrew Walker, Massimo Nicosia, Guido Vetere, Alessandro Faraotti, Marco Monti, Jeff Z. Pan, Honghan Wu, Yuting Zhao, (2017) Question Answering and Knowledge Graphs Exploiting Linked Data and Knowledge Graphs in Large Organisations (doi: 10.1007/978-3-319-45654-6_7); source: Crossref Metadata Search

Wang, H., Sun, Q., Oellrich, A., Wu, H., Dobson, R., (2017) The psycho-ENV corpus: Research articles annotated for knowledge discovery on correlating mental diseases and environmental factors CEUR Workshop Proceedings (eid: 2-s2.0-85029066847)(issn: 16130073); source: Scopus - Elsevier

Honghan Wu, Ronald Denaux, Panos Alexopoulos, Yuan Ren, Jeff Z. Pan, (2017) Understanding Knowledge Graphs Exploiting Linked Data and Knowledge Graphs in Large Organisations (doi: 10.1007/978-3-319-45654-6_6); source: Crossref Metadata Search

Zina M. Ibrahim, Honghan Wu, Robbie Mallah, Richard J. B. Dobson, (2016) Category-Driven Association Rule Mining Research and Development in Intelligent Systems XXXIII (doi: 10.1007/978-3-319-47175-4_2); source: Crossref Metadata Search

Honghan Wu, Zina M. Ibrahim, Ehtesham Iqbal, Richard J. B. Dobson, (2016) Encoding Medication Episodes for Adverse Drug Event Prediction Research and Development in Intelligent Systems XXXIII (doi: 10.1007/978-3-319-47175-4_18); source: Crossref Metadata Search

Yuting Zhao, Guido Vetere, Jeff Z. Pan, Alessandro Faraotti, Marco Monti, Honghan Wu, (2016) Meta-Level Properties for Reasoning on Dynamic Data Semantic Technology (doi: 10.1007/978-3-319-31676-5_19); source: Crossref Metadata Search

Jeff Z. Pan, José Manuel Gómez Pérez, Yuan Ren, Honghan Wu, Haofen Wang, Man Zhu, (2015) Graph Pattern Based RDF Data Compression Semantic Technology (doi: 10.1007/978-3-319-15615-6_18); source: Crossref Metadata Search

Chen, J., Chen, H., Zheng, G., Pan, J.Z., Wu, H., Zhang, N., (2014) Big smog meets web science: Smog disaster analysis based on social media and device data on the web WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web (doi: 10.1145/2567948.2576941)(eid: 2-s2.0-84990997615); source: Scopus - Elsevier

Honghan Wu, Boris Villazon-Terrazas, Jeff Z. Pan, Jose Manuel Gomez-Perez, (2014) Exploiting Semantic Web Datasets: A Graph Pattern Based Approach The Semantic Web and Web Science (doi: 10.1007/978-3-662-45495-4_15); source: Crossref Metadata Search

Wu, H., Villazon-Terrazas, B., Pan, J.Z., Gomez-Perez, J.M., (2014) How redundant is it?-An empirical analysis on linked datasets CEUR Workshop Proceedings (eid: 2-s2.0-84908691394); source: Scopus - Elsevier

Jeff Z. Pan, Yuan Ren, Honghan Wu, Man Zhu, (2013) Query generation for semantic datasets Proceedings of the seventh international conference on Knowledge capture - K-CAP '13 (doi: 10.1145/2479832.2479859); source: Crossref Metadata Search

Ago Luberg, Michael Granitzer, Honghan Wu, Priit Järv, Tanel Tammet, (2012) Information retrieval and deduplication for tourism recommender sightsplanner Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics - WIMS '12 (doi: 10.1145/2254129.2254191); source: Crossref Metadata Search

Honghan Wu, Ago Luberg, Tanel Tammet, (2012) Ranking domain objects bywisdom of web pages Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics - WIMS '12 (doi: 10.1145/2254129.2254210); source: Crossref Metadata Search

Wu, H., Qu, Y., Li, H., (2010) Searching semantic web documents based on RDF sentences Jisuanji Yanjiu yu Fazhan/Computer Research and Development (eid: 2-s2.0-77950556598); source: Scopus - Elsevier

Wu, H., Qu, Y., (2009) Understanding semantic web entity: Concept space based summarization method Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) (doi: 10.3969/j.issn.1001-0505.2009.04.014)(eid: 2-s2.0-69249225557); source: Scopus - Elsevier

Cheng, G., Wu, H., Ge, W., Qu, Y., (2008) Searching Semantic Web objects based on class hierarchies CEUR Workshop Proceedings (eid: 2-s2.0-84885222384); source: Scopus - Elsevier

Hu, W., Zhao, Y., Li, D., Cheng, G., Wu, H., Qu, Y., (2007) Falcon-AO: Results for OAEI 2007 CEUR Workshop Proceedings (eid: 2-s2.0-84868527756); source: Scopus - Elsevier

Supervision

  • Xu, Chao
    Developing a risk stratification tool to detect ADHD in children and adolescents

Professional activities & recognition

Research fellowships

  • 2018 - 2022: Health Data Research UK

Editorial boards

  • 2021: BMC Medical Informatics and Decision Making
  • 2022: BMC Digital Health

Professional & learned societies

  • 2020 - 2023: Turing Fellow, Alan Turing Institute