399 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Rice UniversityUS17 papers
- The University of Texas Health Science Center at HoustonUS17 papers
- The University of Texas at AustinUS13 papers
- Harvard UniversityUS12 papers
- Baylor College of MedicineUS10 papers
- The University of Texas Southwestern Medical CenterUS9 papers
- University of PennsylvaniaUS8 papers
- Massachusetts General HospitalUS7 papers
- Stanford UniversityUS7 papers
- Technical University of MunichDE7 papers
- University of Alabama at BirminghamUS7 papers
- University of North Carolina at Chapel HillUS7 papers
5 papers · 1 filter
Synthesized Annotation Guidelines are Knowledge-Lite Boosters for Clinical Information Extraction
Enshuo Hsu, Martin Ugbala, Krishna Kumar Kookal +4
Generative information extraction using large language models, particularly through few-shot learning, has become a popular method. Recent studies indicate that providing a detaile…
Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?
Hye Sun Yun, Karen Y. C. Zhang, Ramez Kouzy +3
Medical research faces well-documented challenges in translating novel treatments into clinical practice. Publishing incentives encourage researchers to present "positive" findings…
Leveraging Large Language Models for Knowledge-free Weak Supervision in Clinical Natural Language Processing
Enshuo Hsu, Kirk Roberts
The performance of deep learning-based natural language processing systems is based on large amounts of labeled training data which, in the clinical domain, are not easily availabl…
Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations
Yiming Li, Xueqing Peng, Jianfu Li +6
In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like…
Text Classification of Cancer Clinical Trial Eligibility Criteria
Yumeng Yang, Soumya Jayaraj, Ethan B Ludmir +1
Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility is stated in natural language. A potential solution to…