Qingyu Chen


2024

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KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Rui Yang | Haoran Liu | Edison Marrese-Taylor | Qingcheng Zeng | Yuhe Ke | Wanxin Li | Lechao Cheng | Qingyu Chen | James Caverlee | Yutaka Matsuo | Irene Li
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing

Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities. However, their application in real clinical settings is challenging due to potential deviations from medical facts and inherent biases. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) with ranking and re-ranking techniques, aiming to improve free-text question-answering (QA) in the medical domain. Specifically, upon receiving a question, we initially retrieve triplets from a medical KG to gather factual information. Subsequently, we innovatively apply ranking methods to refine the ordering of these triplets, aiming to yield more precise answers. To the best of our knowledge, KG-Rank is the first application of ranking models combined with KG in medical QA specifically for generating long answers. Evaluation of four selected medical QA datasets shows that KG-Rank achieves an improvement of over 18% in the ROUGE-L score. Moreover, we extend KG-Rank to open domains, where it realizes a 14% improvement in ROUGE-L, showing the effectiveness and potential of KG-Rank.

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Yale at “Discharge Me!”: Evaluating Constrained Generation of Discharge Summaries with Unstructured and Structured Information
Vimig Socrates | Thomas Huang | Xuguang Ai | Soraya Fereydooni | Qingyu Chen | R Andrew Taylor | David Chartash
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing

In this work, we propose our top-ranking (2nd place) pipeline for the generation of discharge summary subsections as a part of the BioNLP 2024 Shared Task 2: “Discharge Me!”. We evaluate both encoder-decoder and state-of-the-art decoder-only language models on the generation of two key sections of the discharge summary. To evaluate the ability of NLP methods to further alleviate the documentation burden on physicians, we also design a novel pipeline to generate the brief hospital course directly from structured information found in the EHR. Finally, we evaluate a constrained beam search approach to inject external knowledge about relevant patient problems into the text generation process. We find that a BioBART model fine-tuned on a larger fraction of the data without constrained beam search outperforms all other models.

2020

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Automatic recognition of abdominal lymph nodes from clinical text
Yifan Peng | Sungwon Lee | Daniel C. Elton | Thomas Shen | Yu-xing Tang | Qingyu Chen | Shuai Wang | Yingying Zhu | Ronald Summers | Zhiyong Lu
Proceedings of the 3rd Clinical Natural Language Processing Workshop

Lymph node status plays a pivotal role in the treatment of cancer. The extraction of lymph nodes from radiology text reports enables large-scale training of lymph node detection on MRI. In this work, we first propose an ontology of 41 types of abdominal lymph nodes with a hierarchical relationship. We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports. We demonstrate the superior performance of a model fine-tuned on MRI reports using BlueBERT, called MriBERT. We find that MriBERT outperforms the rule-based labeler (0.957 vs 0.644 in micro weighted F1-score) as well as other BERT-based variations (0.913 - 0.928). We make the code and MriBERT publicly available at https://github.com/ncbi-nlp/bluebert, with the hope that this method can facilitate the development of medical report annotators to produce labels from scratch at scale.

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An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining
Yifan Peng | Qingyu Chen | Zhiyong Lu
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing

Multi-task learning (MTL) has achieved remarkable success in natural language processing applications. In this work, we study a multi-task learning model with multiple decoders on varieties of biomedical and clinical natural language processing tasks such as text similarity, relation extraction, named entity recognition, and text inference. Our empirical results demonstrate that the MTL fine-tuned models outperform state-of-the-art transformer models (e.g., BERT and its variants) by 2.0% and 1.3% in biomedical and clinical domain adaptation, respectively. Pairwise MTL further demonstrates more details about which tasks can improve or decrease others. This is particularly helpful in the context that researchers are in the hassle of choosing a suitable model for new problems. The code and models are publicly available at https://github.com/ncbi-nlp/bluebert.