250 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Xiaoqian Jiang3 profiles5 · h 23
- Hua Xu3 profiles4 · h 7
- Jingcheng Du3 profiles4 · h 21
- Kirk Roberts3 profiles4 · h 36
- Rui Zhang3 profiles3 · h 77
- Yan Hu2 profiles3 · h 9
- Zhiyong Lu2 profiles3 · h 18
- Cui Tao2 profiles2 · h 30
- Fang Li2 profiles2 · h 12
- Jimin Huang2 profiles2 · h 19
- Kalpana Raja2 profiles2 · h 15
- Qingyu Chen2 profiles2 · h 32
- The University of Texas Health Science Center at HoustonUS15 papers
- Yale UniversityUS4 papers
- The University of Texas MD Anderson Cancer CenterUS3 papers
- National Institutes of HealthUS2 papers
- United States National Library of MedicineUS2 papers
- University of Maryland, BaltimoreUS2 papers
- Baylor College of MedicineUS1 paper
- Boston Children's HospitalUS1 paper
- Cornell UniversityUS1 paper
- Foundation for the National Institutes of HealthUS1 paper
- Harvard UniversityUS1 paper
- Houston MethodistUS1 paper
6 papers · 1 filter
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…
A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network
Julian Carvajal Rico, Adel Alaeddini, Syed Hasib Akhter Faruqui +2
Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healt…
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…
GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console
Anindita Nath, Savannah Mwesigwa, Yulin Dai +2
Summary: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackl…
Advancing bioinformatics with large language models: components, applications and perspectives
Jiajia Liu, Mengyuan Yang, Yankai Yu +4
Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language pro…