Papers by Richard Dobson
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation (D19-3)
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| Challenge: | 80% of biomedical data is stored in unstructured text such as electronic health records (EHRs). |
| Approach: | They propose a web-based interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text. |
| Outcome: | The proposed interface is designed to build, improve and customise a NER+L model for biomedical domain text and collate accurate research use case specific training data. |
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
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| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |