Yonghui Wu

Assistant Professor

University of Florida

United States

Research Interests

Explore related searches

Professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Yonghui Wu is an Assistant Professor at the University of Florida, USA. His research interests include the application of electronic health records in clinical prediction models, deep learning for mental health identification, and the utilization of large language models in healthcare analytics. Recent publications highlight his work on lung cancer screening eligibility, pediatric asthma subtypes, and the integration of qualitative methods in health data analysis.

Articles (14)

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2020

Year:

2020

Collaborators (9)

Elizabeth Shenkman

Professor and Chair

University of Florida

UNITED STATES

View details

JIE XU

University of Florida

UNITED STATES

View details

Michelle Alvarado

Assistant Professor

University of Florida

UNITED STATES

View details

Hyehyun Julia Kim

UNITED STATES

View details

Piaopiao Li

University of Florida

UNITED STATES

View details

Jiang Bian

Associate Professor

University of Florida

UNITED STATES

View details

Fernando Bril

The University of Alabama

UNITED STATES

View details

Evangelos Christidis

Professor

University of California Riverside

UNITED STATES

View details

William Hogan

Professor and Director

Medical College of Wisconsin

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Yonghui Wu

Research Interests

Explore related searches

Contact this professor

About

Articles (14)

Toward a Computable Phenotype for Determining Eligibility of Lung Cancer Screening Using Electronic Health Records

Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need

Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study

Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction

Large language models as decision aids in neuro-oncology: a review of shared decision-making applications

Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs

Clinical concept and relation extraction using prompt-based machine reading comprehension

The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records

Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study

Barriers and Facilitators of Obtaining Social Determinants of Health of Patients With Cancer Through the Electronic Health Record Using Natural Language Processing Technology: Qualitative Feasibility Study With Stakeholder Interviews

Noninvasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis Using Machine Learning Methods: Comparative Study With Existing Quantitative Risk Scores

A large language model for electronic health records.

Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models

Measurement of Semantic Textual Similarity in Clinical Texts: Comparison of Transformer-Based Models

Collaborators (9)

Elizabeth Shenkman

JIE XU

Michelle Alvarado

Hyehyun Julia Kim

Piaopiao Li

Jiang Bian

Fernando Bril

Evangelos Christidis

William Hogan

Make your next move.