1 code implementation • 25 Jan 2024 • Yinghao Zhu, Zixiang Wang, Junyi Gao, Yuning Tong, Jingkun An, Weibin Liao, Ewen M. Harrison, Liantao Ma, Chengwei Pan
The inherent complexity of structured longitudinal Electronic Health Records (EHR) data poses a significant challenge when integrated with Large Language Models (LLMs), which are traditionally tailored for natural language processing.
1 code implementation • 7 Jun 2023 • Yinghao Zhu, Jingkun An, Enshen Zhou, Lu An, Junyi Gao, Hao Li, Haoran Feng, Bo Hou, Wen Tang, Chengwei Pan, Liantao Ma
In healthcare AI, these attributes can play a significant role in determining the quality of care that individuals receive.
1 code implementation • 17 Jan 2023 • Liantao Ma, Chaohe Zhang, Junyi Gao, Xianfeng Jiao, Zhihao Yu, Xinyu Ma, Yasha Wang, Wen Tang, Xinju Zhao, Wenjie Ruan, Tao Wang
Here, our objective is to develop a deep learning model for a real-time, individualized, and interpretable mortality prediction model - AICare.
3 code implementations • 16 Sep 2022 • Junyi Gao, Yinghao Zhu, Wenqing Wang, Yasha Wang, Wen Tang, Ewen M. Harrison, Liantao Ma
Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Record (EHR) data.
1 code implementation • 25 Jul 2022 • Junyi Gao, Chaoqi Yang, George Heintz, Scott Barrows, Elise Albers, Mary Stapel, Sara Warfield, Adam Cross, Jimeng Sun, the N3C consortium
We respond to the national Pediatric COVID-19 data challenge with a novel machine learning model, MedML.
no code implementations • 17 Jul 2020 • Liantao Ma, Xinyu Ma, Junyi Gao, Chaohe Zhang, Zhihao Yu, Xianfeng Jiao, Wenjie Ruan, Yasha Wang, Wen Tang, Jiangtao Wang
Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide.
1 code implementation • 15 Jun 2020 • Junyi Gao, Cao Xiao, Lucas M. Glass, Jimeng Sun
The other path processes EHR with multi-granularity memory network that encodes structured patient records into multiple levels based on medical ontology.
1 code implementation • 24 Jan 2020 • Junyi Gao, Cao Xiao, Yasha Wang, Wen Tang, Lucas M. Glass, Jimeng Sun
Compared to the best baseline model, StageNet achieves up to 12% higher AUPRC for risk prediction task on two real-world patient datasets.
1 code implementation • 27 Nov 2019 • Liantao Ma, Junyi Gao, Yasha Wang, Chaohe Zhang, Jiangtao Wang, Wenjie Ruan, Wen Tang, Xin Gao, Xinyu Ma
It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative interpretability.
1 code implementation • 27 Nov 2019 • Liantao Ma, Chaohe Zhang, Yasha Wang, Wenjie Ruan, Jiantao Wang, Wen Tang, Xinyu Ma, Xin Gao, Junyi Gao
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics.
1 code implementation • 18 Mar 2019 • Junyi Gao, Weihao Tan, Liantao Ma, Yasha Wang, Wen Tang
Furthermore, MUSEFood uses the microphone and the speaker to accurately measure the vertical distance from the camera to the food in a noisy environment, thus scaling the size of food in the image to its actual size.