338 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- The University of Texas MD Anderson Cancer CenterUS17 papers
- The University of Texas Health Science CenterUS15 papers
- Yale UniversityUS13 papers
- Rice UniversityUS12 papers
- Fudan UniversityCN7 papers
- Harvard UniversityUS7 papers
- Mayo Clinic in FloridaUS7 papers
- National Institutes of HealthUS6 papers
- University of PennsylvaniaUS6 papers
- Texas A&M UniversityUS5 papers
- Baylor College of MedicineUS4 papers
- Cornell UniversityUS4 papers
6 papers · 2 filters
A Comparative Study of Recent Large Language Models on Generating Hospital Discharge Summaries for Lung Cancer Patients
Yiming Li, Fang Li, Kirk Roberts +3
Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care.…
Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models
Zehan Li, Yan Hu, Scott Lane +7
Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care quality in high-acuity psychiatr…
Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks
Yao-Shun Chuang, Atiquer Rahman Sarkar, Yu-Chun Hsu +2
This study examines integrating EHRs and NLP with large language models (LLMs) to improve healthcare data management and patient care. It focuses on using advanced models to create…
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…
De-identification is not enough: a comparison between de-identified and synthetic clinical notes
Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed +1
For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alt…