Risk of bias assessment in preclinical literature using natural language processing.

ABSTRACT: We sought to apply natural language processing to the task of automatic risk of bias assessment in preclinical literature, which could speed the process of systematic review, provide information to guide research improvement activity, and support translation from preclinical to clinical research. We use 7840 full-text publications describing animal experiments with yes/no annotations for five risk of bias items. We implement a series of models including baselines (support vector machine, logistic regression, random forest), neural models (convolutional neural network, recurrent neural network with attention, hierarchical neural network) and models using BERT with two strategies (document chunk pooling and sentence extraction). We tune hyperparameters to obtain the highest F1 scores for eac

SUBMITTER: Wang Q 

PROVIDER: S-EPMC9298308 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature