1 code implementation • 12 Jul 2024 • Biqing Qi, Kaiyan Zhang, Kai Tian, Haoxiang Li, Zhang-Ren Chen, Sihang Zeng, Ermo Hua, Hu Jinfang, BoWen Zhou
In this paper, we present a comprehensive evaluation of LLMs as biomedical hypothesis generators.
1 code implementation • 6 Jun 2024 • Kaiyan Zhang, Sihang Zeng, Ermo Hua, Ning Ding, Zhang-Ren Chen, Zhiyuan Ma, Haoxin Li, Ganqu Cui, Biqing Qi, Xuekai Zhu, Xingtai Lv, Hu Jinfang, Zhiyuan Liu, BoWen Zhou
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains and are moving towards more specialized areas.
1 code implementation • 13 Dec 2023 • Huaiyuan Ying, Zhengyun Zhao, Yang Zhao, Sihang Zeng, Sheng Yu
Due to a lack of knowledge, previous contrastive learning models trained with Unified Medical Language System (UMLS) synonyms struggle at clustering difficult terms and do not generalize well beyond UMLS terms.
1 code implementation • 10 Nov 2023 • Biqing Qi, Kaiyan Zhang, Haoxiang Li, Kai Tian, Sihang Zeng, Zhang-Ren Chen, BoWen Zhou
We subsequently evaluate the hypothesis generation capabilities of various top-tier instructed models in zero-shot, few-shot, and fine-tuning settings, including both closed and open-source LLMs.
no code implementations • 1 Jul 2023 • Bryan Cai, Sihang Zeng, Yucong Lin, Zheng Yuan, Doudou Zhou, Lu Tian
Electronic health records (EHR) contain narrative notes that provide extensive details on the medical condition and management of patients.
1 code implementation • BioNLP (ACL) 2022 • Sihang Zeng, Zheng Yuan, Sheng Yu
Term clustering is important in biomedical knowledge graph construction.
no code implementations • 18 Mar 2022 • Sheng Yu, Zheng Yuan, Jun Xia, Shengxuan Luo, Huaiyuan Ying, Sihang Zeng, Jingyi Ren, Hongyi Yuan, Zhengyun Zhao, Yucong Lin, Keming Lu, Jing Wang, Yutao Xie, Heung-Yeung Shum
For decades, these knowledge graphs have been developed via expert curation; however, this method can no longer keep up with today's AI development, and a transition to algorithmically generated BioMedKGs is necessary.