| A survey on clinical natural language processing in the United Kingdom from 2007 to 2022 H Wu, M Wang, J Wu, F Francis, YH Chang, A Shavick, H Dong, ... NPJ digital medicine 5 (1), 186, 2022 | 26 | 2022 |
| Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision H Dong, V Suárez-Paniagua, H Zhang, M Wang, E Whitfield, H Wu 2021 43rd Annual International Conference of the IEEE Engineering in …, 2021 | 11 | 2021 |
| Ontology-driven and weakly supervised rare disease identification from clinical notes H Dong, V Suárez-Paniagua, H Zhang, M Wang, A Casey, E Davidson, ... BMC Medical Informatics and Decision Making 23 (1), 86, 2023 | 8 | 2023 |
| Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: a systematic review M Wang, F Francis, H Kunz, X Zhang, C Wan, Y Liu, P Taylor, SH Wild, ... Intelligence-Based Medicine 6, 100072, 2022 | 6 | 2022 |
| Building the Knowledge Graph for UK Health Data Science M Wang, Q Zeng, W Chen, J Pan, H Wu, C Sudlow, D Robertson | 5 | 2020 |
| Knowledge driven phenotyping H Wu, M Wang, Q Zeng, W Chen, JZ Pan, C Sudlow, D Robertson medRxiv, 19013748, 2019 | 4 | 2019 |
| Ontology-based and weakly supervised rare disease phenotyping from clinical notes H Dong, V Suárez-Paniagua, H Zhang, M Wang, A Casey, E Davidson, ... | 3 | 2022 |
| KnowLab at BioCreative VII Track 5 LitCovid: ensemble of deep learning models from diverse sources for COVID-19 literature classification H Dong, M Wang, H Zhang, A Casey, H Wu KnowLab at BioCreative VII Track 5 LitCovid: Ensemble of deep learning …, 2021 | 2 | 2021 |
| Agent-based modelling of pattern formation in pluripotent stem cells: initial experiments and results M Wang, D Robertson, G Blin, S Lowell, T Tsanas 2018 11th International Congress on Image and Signal Processing, BioMedical …, 2018 | 2 | 2018 |
| Quantifying Health Inequalities Induced by Data and AI Models H Wu, M Wang, A Sylolypavan, S Wild arXiv preprint arXiv:2205.01066, 2022 | 1 | 2022 |
| Investigating motility and pattern formation in pluripotent stem cells through agent-based modeling M Wang, A Tsanas, G Blin, D Robertson 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering …, 2019 | 1 | 2019 |
| Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest E Groves, M Wang, Y Abdulle, H Kunz, J Hoelscher-Obermaier, R Wu, ... arXiv preprint arXiv:2312.12989, 2023 | | 2023 |
| Towards automated dermatology triage: deep learning and knowledge-driven approaches M Wang, E Kloczko, A Altayeb, M Farrugia, G Gupta, H Wu, N Hirani Available at SSRN 4385662, 2023 | | 2023 |
| Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model M Wang, A Tsanas, G Blin, D Robertson Scientific Reports 10 (1), 16209, 2020 | | 2020 |
| Assessing Preferred Proximity Between Different Types of Embryonic Stem Cells. M Wang, A Tsanas, G Blin, D Robertson BIOSIGNALS, 377-381, 2020 | | 2020 |
