1 code implementation • 4 Apr 2024 • Anindita Nath, Savannah Mwesigwa, Yulin Dai, Xiaoqian Jiang, Zhongming Zhao
Summary: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge.
no code implementations • 31 Jan 2024 • Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang
In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated the performance of the synthetically generated notes in a clinical domain task, and (iv) proposed a way to mount a membership inference attack where the target model is trained with synthetic data.
no code implementations • 17 Nov 2023 • Yao-Shun Chuang, Chun-Teh Lee, Ryan Brandon, Trung Duong Tran, Oluwabunmi Tokede, Muhammad F. Walji, Xiaoqian Jiang
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 expression (RE) methods.
no code implementations • 17 Nov 2023 • Yao-Shun Chuang, Xiaoqian Jiang, Chun-Teh Lee, Ryan Brandon, Duong Tran, Oluwabunmi Tokede, Muhammad F. Walji
This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt.
no code implementations • 20 Oct 2023 • Can Li, Dejian Lai, Xiaoqian Jiang, Kai Zhang
Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes like age group, gender, and race/ethnicity.
1 code implementation • 4 Sep 2023 • Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Kwei-Herng Lai, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo Costilla Reyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research.
no code implementations • 21 Aug 2023 • Zhuohang Li, Chao Yan, Xinmeng Zhang, Gharib Gharibi, Zhijun Yin, Xiaoqian Jiang, Bradley A. Malin
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks.
no code implementations • 27 Jun 2023 • Tanjida Kabir, Luyao Chen, Muhammad F Walji, Luca Giancardo, Xiaoqian Jiang, Shayan Shams
Learning about diagnostic features and related clinical information from dental radiographs is important for dental research.
no code implementations • 30 Apr 2023 • Kai Zhang, Xiaoqian Jiang
Based on this novel finding, we engineered over 30 features from the metadata of the original features and used machine learning to build classification models to automatically identify PHI fields in structured Electronic Health Record (EHR) data.
1 code implementation • 8 Apr 2023 • Kai Zhang, John A. Lincoln, Xiaoqian Jiang, Elmer V. Bernstam, Shayan Shams
Multiple Sclerosis (MS) is a chronic disease developed in human brain and spinal cord, which can cause permanent damage or deterioration of the nerves.
no code implementations • 5 Apr 2023 • Can Li, Xiaoqian Jiang, Kai Zhang
Specifically, we proposed a deep-learning model to predict multiple risk factors after a liver transplant.
no code implementations • 30 Mar 2023 • Sirui Ding, Qiaoyu Tan, Chia-Yuan Chang, Na Zou, Kai Zhang, Nathan R. Hoot, Xiaoqian Jiang, Xia Hu
Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure.
1 code implementation • 29 Mar 2023 • Yan Hu, Qingyu Chen, Jingcheng Du, Xueqing Peng, Vipina Kuttichi Keloth, Xu Zuo, Yujia Zhou, Zehan Li, Xiaoqian Jiang, Zhiyong Lu, Kirk Roberts, Hua Xu
Results: Using baseline prompts, GPT-3. 5 and GPT-4 achieved relaxed F1 scores of 0. 634, 0. 804 for MTSamples, and 0. 301, 0. 593 for VAERS.
no code implementations • 24 Mar 2023 • Chia-Yuan Chang, Jiayi Yuan, Sirui Ding, Qiaoyu Tan, Kai Zhang, Xiaoqian Jiang, Xia Hu, Na Zou
To tackle these challenges, deep learning frameworks have been created to match patients to trials.
no code implementations • 24 Mar 2023 • Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang, Xia Hu
The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care.
no code implementations • 23 Mar 2023 • Yu-Neng Chuang, Ruixiang Tang, Xiaoqian Jiang, Xia Hu
Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes.
no code implementations • 8 Mar 2023 • Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang, Xia Hu
Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23. 37% to 63. 99% for the named entity recognition task and from 75. 86% to 83. 59% for the relation extraction task.
no code implementations • 18 Feb 2023 • Sirui Ding, Ruixiang Tang, Daochen Zha, Na Zou, Kai Zhang, Xiaoqian Jiang, Xia Hu
To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.
no code implementations • 3 Nov 2022 • Qiuchen Zhang, Jing Ma, Jian Lou, Li Xiong, Xiaoqian Jiang
PATE combines an ensemble of "teacher models" trained on sensitive data and transfers the knowledge to a "student" model through the noisy aggregation of teachers' votes for labeling unlabeled public data which the student model will be trained on.
no code implementations • 1 Aug 2022 • Yifei Ren, Jian Lou, Li Xiong, Joyce C Ho, Xiaoqian Jiang, Sivasubramanium Bhavani
By supervising the tensor factorization with downstream prediction tasks and leveraging information from multiple related predictive tasks, MULTIPAR can yield not only more meaningful phenotypes but also better predictive performance for downstream tasks.
no code implementations • 15 Oct 2021 • Pulakesh Upadhyaya, Kai Zhang, Can Li, Xiaoqian Jiang, Yejin Kim
Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care.
no code implementations • 28 Sep 2021 • Wentao Li, Jiayi Tong, Md. Monowar Anjum, Noman Mohammed, Yong Chen, Xiaoqian Jiang
Objectives: This paper develops two algorithms to achieve federated generalized linear mixed effect models (GLMM), and compares the developed model's outcomes with each other, as well as that from the standard R package (`lme4').
no code implementations • 27 Sep 2021 • Yaobin Ling, Pulakesh Upadhyaya, Luyao Chen, Xiaoqian Jiang, Yejin Kim
We also expect to provide the feasibility of HTE for personalized drug effectiveness.
no code implementations • 24 Sep 2021 • Chun-Teh Lee, Tanjida Kabir, Jiman Nelson, Sally Sheng, Hsiu-Wan Meng, Thomas E. Van Dyke, Muhammad F. Walji, Xiaoqian Jiang, Shayan Shams
Conclusion: The proposed DL model provides reliable RBL measurements and image-based periodontal diagnosis using periapical radiographic images.
no code implementations • 10 Sep 2021 • Kai Zhang, Chao Tian, Kun Zhang, Todd Johnson, Xiaoqian Jiang
The PC algorithm is the state-of-the-art algorithm for causal structure discovery on observational data.
no code implementations • 18 Aug 2021 • Md Monowar Anjum, Noman Mohammed, Xiaoqian Jiang
In this work, we propose a novel problem formulation for de-identification of unstructured clinical text.
no code implementations • 4 Aug 2021 • Greg M. Silverman, Raymond L. Finzel, Michael V. Heinz, Jake Vasilakes, Jacob C. Solinsky, Reed McEwan, Benjamin C. Knoll, Christopher J. Tignanelli, Hongfang Liu, Hua Xu, Xiaoqian Jiang, Genevieve B. Melton, Serguei VS Pakhomov
Our objective in this study is to investigate the behavior of Boolean operators on combining annotation output from multiple Natural Language Processing (NLP) systems across multiple corpora and to assess how filtering by aggregation of Unified Medical Language System (UMLS) Metathesaurus concepts affects system performance for Named Entity Recognition (NER) of UMLS concepts.
1 code implementation • 4 Jul 2021 • Yan Ding, Xiaoqian Jiang, Yejin Kim
The RGCN model achieved an overall accuracy of 0. 872, an AUROC of 0. 919 and an AUPRC of 0. 838 for the testing dataset with the drug-protein interactions and the Mordred descriptors as the input.
no code implementations • 11 Mar 2021 • Hannah Lei, Weiqi Lu, Alan Ji, Emmett Bertram, Paul Gao, Xiaoqian Jiang, Arko Barman
Many COVID-19 patients developed prolonged symptoms after the infection, including fatigue, delirium, and headache.
no code implementations • 1 Feb 2021 • Kai Zhang, Siddharth Karanth, Bela Patel, Robert Murphy, Xiaoqian Jiang
We propose a novel in-time risk trajectory predictive model to handle the irregular sampling rate in the data, which follows the dynamics of risk of performing mechanical ventilation for individual patients.
no code implementations • 6 Oct 2020 • Yuqi Si, Jingcheng Du, Zhao Li, Xiaoqian Jiang, Timothy Miller, Fei Wang, W. Jim Zheng, Kirk Roberts
We show the importance and feasibility of learning comprehensive representations of patient EHR data through a systematic review.
no code implementations • 23 Sep 2020 • Kanglin Hsieh, Yinyin Wang, Luyao Chen, Zhongming Zhao, Sean Savitz, Xiaoqian Jiang, Jing Tang, Yejin Kim
In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment.
no code implementations • 22 May 2020 • Yeongjae Gil, Xiaoqian Jiang, Miran Kim, Junghye Lee
Data integration and sharing maximally enhance the potential for novel and meaningful discoveries.
no code implementations • LREC 2020 • Xiaojing Yu, Tianlong Chen, Zhengjie Yu, Huiyu Li, Yang Yang, Xiaoqian Jiang, Anxiao Jiang
Compared to existing datasets, the queries in the dataset here are derived from the eligibility criteria of clinical trials and include \textit{Order-sensitive, Counting-based, and Boolean-type} cases which are not seen before.
no code implementations • 26 Aug 2019 • Jing Ma, Qiuchen Zhang, Jian Lou, Joyce C. Ho, Li Xiong, Xiaoqian Jiang
We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR.
no code implementations • 14 May 2019 • Xiaoqian Jiang, Samden Lhatoo, Guo-Qiang Zhang, Luyao Chen, Yejin Kim
Existing studies consider Alzheimer's disease (AD) a comorbidity of epilepsy, but also recognize epilepsy to occur more frequently in patients with AD than those without.
no code implementations • 14 May 2019 • Yejin Kim, Xiaoqian Jiang, Luyao Chen, Xiaojin Li, Licong Cui
Sleep change is commonly reported in Alzheimer's disease (AD) patients and their brain wave studies show decrease in dreaming and non-dreaming stages.
no code implementations • 14 May 2019 • Rui Zhang, Luca Giancardo, Danilo A. Pena, Yejin Kim, Hanghang Tong, Xiaoqian Jiang
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD).
no code implementations • 7 Nov 2018 • Yikuan Li, Liang Yao, Chengsheng Mao, Anand Srivastava, Xiaoqian Jiang, Yuan Luo
We developed data-driven prediction models to estimate the risk of new AKI onset.
1 code implementation • 18 Sep 2018 • Jian Liang, Ziqi Liu, Jiayu Zhou, Xiaoqian Jiang, Chang-Shui Zhang, Fei Wang
Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together.
no code implementations • 11 Apr 2017 • Yejin Kim, Jimeng Sun, Hwanjo Yu, Xiaoqian Jiang
In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data.
1 code implementation • 7 Mar 2017 • Md Nazmus Sadat, Md Momin Al Aziz, Noman Mohammed, Feng Chen, Shuang Wang, Xiaoqian Jiang
In this article, we present SAFETY, a hybrid framework, which can securely perform GWAS on federated genomic datasets using homomorphic encryption and recently introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure high efficiency and privacy at the same time.
Cryptography and Security