In this research, we explored the improvement in terms of multi-class disease classification via pre-trained language models over Medical-Abstracts-TC-Corpus that spans five medical conditions. We excluded non-cancer conditions and examined four specific diseases. We assessed four LLMs, BioBERT, XLNet, and BERT, as well as a novel base model (Last-BERT). BioBERT, which was pre-trained on medical data, demonstrated superior performance in medical text classification (97% accuracy). Surprisingly, XLNet followed closely (96% accuracy), demonstrating its generalizability across domains even though it was not pre-trained on medical data. LastBERT, a custom model based on the lighter version of BERT, also proved competitive with 87.10% accuracy (just under BERT's 89.33%). Our findings confirm the importance of specialized models such as BioBERT and also support impressions around more general solutions like XLNet and well-tuned transformer architectures with fewer parameters (in this case, LastBERT) in medical domain tasks.

本研究解决了多类疾病分类准确性不足的问题，通过对比分析了四种不同的预训练语言模型在五种医疗条件下的表现。研究发现，BioBERT在医疗文本分类中表现最佳，达到97%的准确率，而XLNet在未专门针对医疗数据预训练的情况下也展现出优秀的通用性，准确率为96%。这些结果表明，使用专业模型和通用解决方案对于医疗领域的任务提升具有重要意义。

增强多类疾病分类：利用先进的大语言模型对肿瘤、心血管、神经系统和消化系统疾病进行分类