Natural language processing was effective in assisting rapid title and abstract screening when updating systematic reviews

Objectives: To examine whether the use of natural language processing (NLP) technology is effective in assisting rapid title and abstract screening when updating a systematic review.Study Design: Using the searched literature from a published systematic review, we trained and tested an NLP model that enables rapid title and abstract screening when updating a systematic review. The model was a light gradient boosting machine (LightGBM), an ensemble learning classifier which integrates four pretrained Bidirectional Encoder Representations from Transformers (BERT) models.

Source: Journal of Clinical Epidemiology - January 20, 2021 Category: Epidemiology Authors: Xuan Qin, Jiali Liu, Yuning Wang, Yanmei Liu, Ke Deng, Yu Ma, Kang Zou, Ling Li, Xin Sun Source Type: research