Search Results for author: Cui Tao

Found 19 papers, 3 papers with code

Advancing Pancreatic Cancer Prediction with a Next Visit Token Prediction Head on top of Med-BERT

no code implementations3 Jan 2025 Jianping He, Laila Rasmy, Degui Zhi, Cui Tao

Methods: We utilized Med-BERT, an EHR-specific foundation model, and reformulated the disease binary prediction task into a token prediction task and a next visit mask token prediction task to align with Med-BERT's pretraining task format in order to improve the accuracy of pancreatic cancer (PaCa) prediction in both few-shot and fully supervised settings.

Binary Classification Disease Prediction +1

Prompting Large Language Models for Clinical Temporal Relation Extraction

no code implementations4 Dec 2024 Jianping He, Laila Rasmy, Haifang Li, Jianfu Li, Zenan Sun, Evan Yu, Degui Zhi, Cui Tao

We explored four fine-tuning strategies for GatorTron-Base: (1) Standard Fine-Tuning, (2) Hard-Prompting with Unfrozen LLMs, (3) Soft-Prompting with Frozen LLMs, and (4) Low-Rank Adaptation (LoRA) with Frozen LLMs.

Decoder Quantization +2

A Comparative Study of Recent Large Language Models on Generating Hospital Discharge Summaries for Lung Cancer Patients

no code implementations6 Nov 2024 Yiming Li, Fang Li, Kirk Roberts, Licong Cui, Cui Tao, Hua Xu

Evaluation metrics included token-level analysis (BLEU, ROUGE-1, ROUGE-2, ROUGE-L) and semantic similarity scores between model-generated summaries and physician-written gold standards.

Semantic Similarity Semantic Textual Similarity

Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources

no code implementations26 Jun 2024 Yiming Li, Deepthi Viswaroopan, William He, Jianfu Li, Xu Zuo, Hua Xu, Cui Tao

This study aims to evaluate the effectiveness of LLMs and traditional deep learning models in AE extraction, and to assess the impact of ensembling these models on performance.

Deep Learning Event Extraction +3

Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations

no code implementations8 Apr 2024 Yiming Li, Xueqing Peng, Jianfu Li, Xu Zuo, Suyuan Peng, Donghong Pei, Cui Tao, Hua Xu, Na Hong

This study underscores the effectiveness of LLMs like GPT in extracting relations related to acupoint locations, with implications for accurately modeling acupuncture knowledge and promoting standard implementation in acupuncture training and practice.

Relation Relation Extraction

AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events

no code implementations28 Sep 2023 Yiming Li, Jianfu Li, Jianping He, Cui Tao

Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs).

Distributionally Robust Cross Subject EEG Decoding

no code implementations19 Aug 2023 Tiehang Duan, Zhenyi Wang, Gianfranco Doretto, Fang Li, Cui Tao, Donald Adjeroh

In this work, we propose a principled approach to perform dynamic evolution on the data for improvement of decoding robustness.

Data Augmentation Decoder +2

Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

no code implementations18 Jun 2023 Fang Li, Yi Nian, Zenan Sun, Cui Tao

Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine.

Graph Representation Learning Survey

Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research

no code implementations21 Mar 2023 Muhammad Amith, Licong Cui, Kirk Roberts, Cui Tao

Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc.

Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing

no code implementations17 Feb 2022 Yi Nian, Xinyue Hu, Rui Zhang, Jingna Feng, Jingcheng Du, Fang Li, Yong Chen, Cui Tao

The 1, 672, 110 filtered triples were used to train with knowledge graph completion algorithms (i. e., TransE, DistMult, and ComplEx) to predict candidates that might be helpful for AD treatment or prevention.

Graph Mining

Semantic based model of Conceptual Work Products for formal verification of complex interactive systems

no code implementations4 Aug 2020 Mohcine Madkour, Keith Butler, Eric Mercer, Ali Bahrami, Cui Tao

As a first step, we illustrate how graphical class and state diagrams from UML can be developed and critiqued with subject matter experts to serve as specifications of the conceptual work product of case management.

Management

Towards an Ontology-based Medication Conversational Agent for PrEP and PEP

no code implementations WS 2020 Muhammad Amith, Licong Cui, Kirk Roberts, Cui Tao

ABSTRACT: HIV (human immunodeficiency virus) can damage a human{'}s immune system and cause Acquired Immunodeficiency Syndrome (AIDS) which could lead to severe outcomes, including death.

Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction

1 code implementation22 May 2020 Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, Degui Zhi

Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks.

Disease Prediction Prediction

Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States

no code implementations6 Jul 2019 Hansi Zhang, Christopher Wheldon, Adam G. Dunn, Cui Tao, Jinhai Huo, Rui Zhang, Mattia Prosperi, Yi Guo, Jiang Bian

We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS).

ML-Net: multi-label classification of biomedical texts with deep neural networks

4 code implementations13 Nov 2018 Jingcheng Du, Qingyu Chen, Yifan Peng, Yang Xiang, Cui Tao, Zhiyong Lu

Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems.

Benchmarking Feature Engineering +5

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