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This research on data extraction methods applies recent advances in naturallanguage processing to evidence synthesis based on medical texts. Texts ofinterest include abstracts of clinical trials in English and in multilingualcontexts. The main focus is on information characterized via the Population,Intervention, Comparator, and Outcome (PICO) framework, but data extraction isnot limited to these fields. Recent neural network architectures based ontransformers show capacities for transfer learning and increased performance ondownstream natural language processing tasks such as universal readingcomprehension, brought forward by this architecture's use of contextualizedword embeddings and self-attention mechanisms. This paper contributes tosolving problems related to ambiguity in PICO sentence prediction tasks, aswell as highlighting how annotations for training named entity recognitionsystems are used to train a high-performing, but nevertheless flexiblearchitecture for question answering in systematic review automation.Additionally, it demonstrates how the problem of insufficient amounts oftraining annotations for PICO entity extraction is tackled by augmentation. Allmodels in this paper were created with the aim to support systematic review(semi)automation. They achieve high F1 scores, and demonstrate the feasibilityof applying transformer-based classification methods to support data mining inthe biomedical literature.

Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks