Xiaoqian Jiang

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The University of Texas Health Science Center at Houston

United States

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Xiaoqian Jiang is a Professor at The University of Texas Health Science Center at Houston, USA. His research areas encompass machine learning applications in healthcare, particularly in heart transplant outcomes and pediatric stroke research, as well as the ethical implications of artificial intelligence in clinical settings. Recent publications include studies on diagnostic decision support systems, electronic health record data extraction, and innovative privacy solutions related to large language models.

Recent Grants

Grant: Close

CALIFORNIA: Cloud-agnostic Architecture to Locate Indexed FAIR Objects and safely Reuse them in New Integrated Analyses

Open Date: 2017-09-30

Close Date: 2018-06-30

Grant: Close

CALIFORNIA: Cloud-agnostic Architecture to Locate Indexed FAIR Objects and safely Reuse them in New Integrated Analyses

Open Date: 2017-09-30

Close Date: 2018-06-30

Grant: Close

Open Health Natural Language Processing Collaboratory

Open Date: 2017-09-01

Close Date: 2022-08-31

Grant: Close

Decentralized differentially-private methods for dynamic data release and analysis

Open Date: 2017-01-01

Close Date: 2020-12-31

Grant: Close

SERGEANT: SEcuRe GEnome Analysis competition

Open Date: 2016-09-12

Close Date: 2018-06-30

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UNITED STATES

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Tsung-Ting Kuo

University of California, San Diego

UNITED STATES

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yulin dai

The University of Texas Health Science Center at Houston

UNITED STATES

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Raymond Greenberg

Professor

University of Texas Southwestern Medical Center

UNITED STATES

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Paul E. Schulz

The University of Texas Health Science Center at Houston

UNITED STATES

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Bradley Malin

Vanderbilt University Medical Center

UNITED STATES

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Yan Guo

University of Miami

UNITED STATES

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Guo-Hao Lin

Associate Professor

University of California, San Francisco

UNITED STATES

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Xiaoqian Jiang

Research Interests

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About

Recent Grants

Articles (27)

Discover important donor-recipient risk factors and interactions in heart transplant primary graft dysfunction with machine learning

Computerized diagnostic decision support systems—Isabel Pro versus ChatGPT-4 part II

Cross-institutional dental electronic health record entity extraction via generative artificial intelligence and synthetic notes

Robust privacy amidst innovation with large language models through a critical assessment of the risks

Generalizable self-supervised learning for brain CTA in acute stroke

Generative Pre-trained Transformer for Pediatric Stroke Research: A Pilot Study

Improving large language models for clinical named entity recognition via prompt engineering

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction

Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach

Social and Behavior Factors of Alzheimer's Disease and Related Dementias: A National Study in the U.S.

SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization

CancerGPT for few shot drug pair synergy prediction using large pretrained language models

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console

Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process

Clinical outcome-guided deep temporal clustering for disease progression subtyping

Using artificial intelligence to learn optimal regimen plan for Alzheimer’s disease

Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine

Do vaccinations influence the development of Alzheimer disease?

Multi-task learning with dynamic re-weighting to achieve fairness in healthcare predictive modeling

Privacy preserving identification of population stratification for collaborative genomic research

Prediction of Brain Metastases Development in Patients With Lung Cancer by Explainable Artificial Intelligence From Electronic Health Records

A hierarchical strategy to minimize privacy risk when linking “De-identified” data in biomedical research consortia

A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors

Privacy-preserving federated genome-wide association studies via dynamic sampling

Relational graph convolutional networks for predicting blood–brain barrier penetration of drug molecules

Drug-Target Network Study Reveals the Core Target-Protein Interactions of Various COVID-19 Treatments

Factors Associated With COVID-19 Death in the United States: Cohort Study

Collaborators (12)

Stuart Fraser

Na Zou

Wen Li

Yejin Kim

Zhongming Zhao

Tsung-Ting Kuo

yulin dai

Raymond Greenberg

Paul E. Schulz

Bradley Malin

Yan Guo

Guo-Hao Lin

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