2024

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)

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, (doi: 10.1145/3664615) (In Press)

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(1), 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: 10.48550/arXiv.2203.11092) (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)

Articles

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)

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, (doi: 10.1145/3664615) (In Press)

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(1), 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: 10.48550/arXiv.2203.11092) (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]