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
24 papers · 1 filter
Bayesian Nonparametric Clustering with Feature Selection for Spatially Resolved Transcriptomics Data
Bencong Zhu, Guanyu Hu, Yang Xie +3
The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profilin…
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to influence the risk, severity, an…
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…
Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based vector-on-matrix regression
Tong Lu, Yuan Zhang, Vince Lyzinski +4
The joint analysis of multimodal neuroimaging data is critical in the field of brain research because it reveals complex interactive relationships between neurobiological structure…
Multiple Imputation Method for High-Dimensional Neuroimaging Data
Tong Lu, Chixiang Chen, Hsin-Hsiung Huang +3
Missingness is a common issue for neuroimaging data, and neglecting it in downstream statistical analysis can introduce bias and lead to misguided inferential conclusions. It is th…
Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification
Kareem A. Wahid, Carlos E. Cardenas, Barbara Marquez +8
Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three…
FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation
Can Li, Dejian Lai, Xiaoqian Jiang +1
Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes such as age group, gender, and race/ethnicity. Machine learning models for ou…
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…
Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction
Xinyue Hu, Zenan Sun, Yi Nian +6
Background: Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US, underlining the importance of accurate ADRD risk prediction. While…
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
Zhuohang Li, Chao Yan, Xinmeng Zhang +4
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generali…
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…
An F-ratio-Based Method for Estimating the Number of Active Sources in MEG
Amita Giri, John C. Mosher, Amir Adler +1
Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordin…
Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records
Kai Zhang, Xiaoqian Jiang
In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuab…
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…
A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors
Can Li, Xiaoqian Jiang, Kai Zhang
Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for…
Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
Sirui Ding, Qiaoyu Tan, Chia-yuan Chang +5
Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant pro…
Local Contrastive Learning for Medical Image Recognition
S. A. Rizvi, R. Tang, X. Jiang +2
The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised framework…
Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
Chia-Yuan Chang, Jiayi Yuan, Sirui Ding +5
Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To…
Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research
Muhammad Amith, Licong Cui, Kirk Roberts +1
Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health ag…
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…
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
Sirui Ding, Ruixiang Tang, Daochen Zha +4
Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Diseas…
Deep Learning Provides Rapid Screen for Breast Cancer Metastasis with Sentinel Lymph Nodes
Kareem Allam, Xiaohong Iris Wang, Songlin Zhang +6
Deep learning has been shown to be useful to detect breast cancer metastases by analyzing whole slide images of sentinel lymph nodes. However, it requires extensive scanning and an…