no code implementations • 16 Jul 2023 • Jingqing Zhang, Kai Sun, Akshay Jagadeesh, Mahta Ghahfarokhi, Deepa Gupta, Ashok Gupta, Vibhor Gupta, Yike Guo
Recent studies have demonstrated promising performance of ChatGPT and GPT-4 on several medical domain tasks.
no code implementations • 24 May 2022 • Heng-Yi Wu, Jingqing Zhang, Julia Ive, Tong Li, Vibhor Gupta, Bingyuan Chen, Yike Guo
Structured (tabular) data in the preclinical and clinical domains contains valuable information about individuals and an efficient table-to-text summarization system can drastically reduce manual efforts to condense this data into reports.
no code implementations • 18 May 2022 • Jingqing Zhang, Atri Sharma, Luis Bolanos, Tong Li, Ashwani Tanwar, Vibhor Gupta, Yike Guo
This paper proposes a scalable workflow which leverages both structured data and unstructured textual notes from EHRs with techniques including NLP, AutoML and Clinician-in-the-Loop mechanism to build machine learning classifiers to identify patients at scale with given diseases, especially those who might currently be miscoded or missed by ICD codes.
no code implementations • 19 Apr 2022 • Ashwani Tanwar, Jingqing Zhang, Julia Ive, Vibhor Gupta, Yike Guo
Extracting phenotypes from clinical text has been shown to be useful for a variety of clinical use cases such as identifying patients with rare diseases.
no code implementations • EMNLP 2021 • Jingqing Zhang, Luis Bolanos, Tong Li, Ashwani Tanwar, Guilherme Freire, Xian Yang, Julia Ive, Vibhor Gupta, Yike Guo
Contextualised word embeddings is a powerful tool to detect contextual synonyms.
no code implementations • 24 Jul 2021 • Jingqing Zhang, Luis Bolanos, Ashwani Tanwar, Julia Ive, Vibhor Gupta, Yike Guo
We propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information, which is complementary to typically used vital signs and laboratory test results, to predict outcomes in the Intensive Care Unit (ICU).
16 code implementations • ICML 2020 • Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu
Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization.
Ranked #1 on Abstractive Text Summarization on AESLC
1 code implementation • 10 Nov 2019 • Jingqing Zhang, Xiao-Yu Zhang, Kai Sun, Xian Yang, Chengliang Dai, Yike Guo
The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis.
4 code implementations • 17 Aug 2019 • Xiao-Yu Zhang, Jingqing Zhang, Kai Sun, Xian Yang, Chengliang Dai, Yike Guo
The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier.
2 code implementations • NAACL 2019 • Jingqing Zhang, Piyawat Lertvittayakumjorn, Yike Guo
Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification.
1 code implementation • 13 Jun 2018 • Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, Fei Wu
Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.
Ranked #1 on Traffic Prediction on Q-Traffic
no code implementations • 27 Mar 2017 • Yuanhan Mo, Fangde Liu, Douglas McIlwraith, Guang Yang, Jingqing Zhang, Taigang He, Yike Guo
Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge.
no code implementations • 20 Mar 2017 • Hao Dong, Jingqing Zhang, Douglas McIlwraith, Yike Guo
We demonstrate that %the capability of our method to understand the sentence descriptions, so as to I2T2I can generate better multi-categories images using MSCOCO than the state-of-the-art.