Recognizing PICO elements in medical text based on bidirectional GRU neural network
GONG Lejun1,2*,YAO Lingfeng1,GAO Zhihong3,LI Huakang4,5
(1 School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu, China;2 Jiangsu Key Lab of Big Data Security & Intelligent Processing, Nanjing 210023, Jiangsu, China;3 Zhejiang Engineering Research Center of Intelligent Medicine, Wenzhou 325035, Zhejiang, China;4 Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518034, Guangdong, China;5 Suzhou Privacy Information Technology Company, Suzhou 215011, Jiangsu, China)
Abstract:
The traditional learning model of machine learning has the problem of insufficient feature extraction when recognizing PICO components.A GRUCM model that automatically recognizes PICO elements in medical texts is proposed. This model combines the advantage of bidirectional gated recurrent unit ( BiGRU ) neural network and conditional random field(CRF). Not only the problem of insufficient feature extraction in traditional machine learning models can be improved, but also multiple elements can be extracted at the same time, avoiding the waste of resources caused by creating multiple models. Using the test dataset, the F1 of the P element is 88.24%, the F1 of the I element is 80.49% and the F1 of the O element is 86.62%. The recognition effect of long short-term memory(LSTM) neural network and CRF model is compared and analyzed, which shows that the proposed GRUCM model is more effective for PICO elements recognition.
KeyWords:
evidence based medicine; GRUCM model; PICO elements; bi-gated recurrent unit; neural networks