Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

BMC Medical Informatics and Decision Making

Ge Lin¹  Tianyong Hao²  Likeng Liang²  Yibin Xu³  Kun Zeng³ 

[1] National Engineering Research Center of Digital Life, Sun Yat-Sen University, Guangzhou, China;School of Computer Science, South China Normal University, Guangzhou, China;School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China;

关键词: Eligibility criteria classification;    Metric learning;    Focal loss;    Ensemble learning;    Clinical trial;   

DOI  :  10.1186/s12911-021-01492-z

来源: Springer

【 摘 要 】

BackgroundEligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data.MethodsAn ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories.ResultsOur ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement.ConclusionsA model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

【 授权许可】

CC BY