Our team, UTSolve, participated in the Medical Natural Language Processing for AI Chat (MedNLP-CHAT) task~https: //sociocom. naist. jp/mednlp-chat/ at NTCIR-18. The task involved classifying various medical texts into medical, ethical, and legal risks. In this report, we utilized BioBERT, a pre-trained biomedical language model that was trained on a large amount of biological text data to predict the risk level of medical texts. We also evaluated the medical and clinical language models MedBERT and ClinicalBERT. Based on prediction performance, BioBERT achieved the best classification results, with a weighted F1 score of 0. 7812 for medical risk, 0. 8629 for ethical risk, and 0. 7288 for legal risk.
Cheng et al. (Fri,) studied this question.