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
26 papers · 1 filter
Clinical Document Metadata Extraction: A Scoping Review
Kurt Miller, Qiuhao Lu, William Hersh +4
Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information capture…
Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications
Anran Li, Lingfei Qian, Mengmeng Du +18
Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to e…
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…
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…
Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records
Yao-Shun Chuang, Xiaoqian Jiang, Chun-Teh Lee +4
This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT…
AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events
Yiming Li, Jianfu Li, Jianping He +1
Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Langua…
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…
Identifying and Extracting Rare Disease Phenotypes with Large Language Models
Cathy Shyr, Yan Hu, Paul A. Harris +1
Rare diseases (RDs) are collectively common and affect 300 million people worldwide. Accurate phenotyping is critical for informing diagnosis and treatment, but RD phenotypes are o…
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
Kai Zhang, Rong Zhou, Eashan Adhikarla +20
Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to ut…
Benchmarking large language models for biomedical natural language processing applications and recommendations
Qingyu Chen, Yan Hu, Xueqing Peng +18
The rapid growth of biomedical literature poses challenges for manual knowledge curation and synthesis. Biomedical Natural Language Processing (BioNLP) automates the process. While…
Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching
Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang +1
The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges s…
Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans
Avijit Mitra, Richeek Pradhan, Rachel D Melamed +8
Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic he…
Automated Identification of Eviction Status from Electronic Health Record Notes
Zonghai Yao, Jack Tsai, Weisong Liu +4
Objective: Evictions are important social and behavioral determinants of health. Evictions are associated with a cascade of negative events that can lead to unemployment, housing i…
Deep learning-based NLP Data Pipeline for EHR Scanned Document Information Extraction
Enshuo Hsu, Ioannis Malagaris, Yong-Fang Kuo +2
Scanned documents in electronic health records (EHR) have been a challenge for decades, and are expected to stay in the foreseeable future. Current approaches for processing often…
COVID-19 Smart Chatbot Prototype for Patient Monitoring
Hannah Lei, Weiqi Lu, Alan Ji +4
Many COVID-19 patients developed prolonged symptoms after the infection, including fatigue, delirium, and headache. The long-term health impact of these conditions is still not cle…
Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data
Yuqi Si, Elmer V Bernstam, Kirk Roberts
The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task p…
Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping
Surabhi Datta, Shekhar Khanpara, Roy F. Riascos +1
Classifying fine-grained ischemic stroke phenotypes relies on identifying important clinical information. Radiology reports provide relevant information with context to determine s…
RadLex Normalization in Radiology Reports
Surabhi Datta, Jordan Godfrey-Stovall, Kirk Roberts
Radiology reports have been widely used for extraction of various clinically significant information about patients' imaging studies. However, limited research has focused on stand…
COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model
Jingqi Wang, Noor Abu-el-rub, Josh Gray +8
The COVID-19 pandemic swept across the world rapidly, infecting millions of people. An efficient tool that can accurately recognize important clinical concepts of COVID-19 from fre…