Zobrazeno 1 - 10
of 1 244
pro vyhledávání: '"Jiang, Xiaoqian"'
Autor:
Chuang, Yao-Shun, Lee, Chun-Teh, Tokede, Oluwabunmi, Lin, Guo-Hao, Brandon, Ryan, Tran, Trung Duong, Jiang, Xiaoqian, Walji, Muhammad F.
This research addresses the issue of missing structured data in dental records by extracting diagnostic information from unstructured text. The updated periodontology classification system's complexity has increased incomplete or missing structured d
Externí odkaz:
http://arxiv.org/abs/2407.21050
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 secure, HIPAA-compliant synthetic patient notes for biomedical resear
Externí odkaz:
http://arxiv.org/abs/2407.16166
Heterogeneous graphs can well describe the complex entity relationships in the real world. For example, online shopping networks contain multiple physical types of consumers and products, as well as multiple relationship types such as purchasing and
Externí odkaz:
http://arxiv.org/abs/2407.00708
In the era of big data, access to abundant data is crucial for driving research forward. However, such data is often inaccessible due to privacy concerns or high costs, particularly in healthcare domain. Generating synthetic (tabular) data can addres
Externí odkaz:
http://arxiv.org/abs/2406.10521
Summary: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation
Externí odkaz:
http://arxiv.org/abs/2404.04299
Publikováno v:
Journal of Medical Internet Research, Vol 23, Iss 2, p e24486 (2021)
BackgroundOpioid use disorder presents a public health issue afflicting millions across the globe. There is a pressing need to understand the opioid supply chain to gain new insights into the mitigation of opioid use and effectively combat the opioid
Externí odkaz:
https://doaj.org/article/11c4233496a849418dbd7dd1931d610c
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 alternative. Recent successes with numerical and tabular data generative
Externí odkaz:
http://arxiv.org/abs/2402.00179
Autor:
Chuang, Yao-Shun, Jiang, Xiaoqian, Lee, Chun-Teh, Brandon, Ryan, Tran, Duong, Tokede, Oluwabunmi, Walji, Muhammad F.
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-J models was utilized to directly test the gold standard as well as t
Externí odkaz:
http://arxiv.org/abs/2311.10810
Autor:
Chuang, Yao-Shun, Lee, Chun-Teh, Brandon, Ryan, Tran, Trung Duong, Tokede, Oluwabunmi, Walji, Muhammad F., Jiang, Xiaoqian
This study aimed to utilize text processing and natural language processing (NLP) models to mine clinical notes for the diagnosis of periodontitis and to evaluate the performance of a named entity recognition (NER) model on different regular expressi
Externí odkaz:
http://arxiv.org/abs/2311.10809
FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation
Publikováno v:
AMIA Jt Summits Transl Sci Proc. 2024 May 31;2024:593-602. PMID: 38827050; PMCID: PMC11141863
Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes such as age group, gender, and race/ethnicity. Machine learning models for outcome prediction can introduce additional biases. Therefore, we introd
Externí odkaz:
http://arxiv.org/abs/2310.13820