Authors:
Lena Schmidt
1
;
2
;
Julie Weeds
2
and
Julian P. T. Higgins
1
Affiliations:
1
University of Bristol, Bristol Medical School, 39 Whatley Road, BS82PS Bristol, U.K.
;
2
University of Sussex, Department of Informatics, BN19QJ Brighton, U.K.
Keyword(s):
BERT, Data mining, Evidence-based Medicine, PICO Element Detection, Natural Language Processing, Question Answering, Sentence Classification, Systematic Review Automation, Transformer Neural Network.
Abstract:
This research on data extraction methods applies recent advances in natural language processing to evidence
synthesis based on medical texts. Texts of interest include abstracts of clinical trials in English and in multilingual contexts. The main focus is on information characterized via the Population, Intervention, Comparator,
and Outcome (PICO) framework, but data extraction is not limited to these fields. Recent neural network
architectures based on transformers show capacities for transfer learning and increased performance on downstream natural language processing tasks such as universal reading comprehension, brought forward by this
architecture’s use of contextualized word embeddings and self-attention mechanisms. This paper contributes
to solving problems related to ambiguity in PICO sentence prediction tasks, as well as highlighting how annotations for training named entity recognition systems are used to train a high-performing, but nevertheless
flexible architecture
for question answering in systematic review automation. Additionally, it demonstrates
how the problem of insufficient amounts of training annotations for PICO entity extraction is tackled by augmentation. All models in this paper were created with the aim to support systematic review (semi)automation.
They achieve high F1 scores, and demonstrate the feasibility of applying transformer-based classification
methods to support data mining in the biomedical literature.
(More)