ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context

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.

AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration

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.

Representation Learning

M$^3$Care: Learning with Missing Modalities in Multimodal Healthcare Data

1 code implementation • 28 Oct 2022 • Chaohe Zhang, Xu Chu, Liantao Ma, Yinghao Zhu, Yasha Wang, Jiangtao Wang, Junfeng Zhao

M3Care is an end-to-end model compensating the missing information of the patients with missing modalities to perform clinical analysis.

Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset

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.

Feature Importance Mortality Prediction

Predict and Interpret Health Risk using EHR through Typical Patients

1 code implementation • 18 Dec 2023 • Zhihao Yu, Chaohe Zhang, Yasha Wang, Wen Tang, Jiangtao Wang, Liantao Ma

Predicting health risks from electronic health records (EHR) is a topic of recent interest.

CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis

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.

Knowledge Distillation Length-of-Stay prediction +1

Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets

no code implementations • 11 Oct 2023 • Zhongji Zhang, Yuhang Wang, Yinghao Zhu, Xinyu Ma, Tianlong Wang, Chaohe Zhang, Yasha Wang, Liantao Ma

Due to the limited information about emerging diseases, symptoms are hard to be noticed and recognized, so that the window for clinical intervention could be ignored.

Ethics Representation Learning +1