Imputation with Inter-Series Information from Prototypes for Irregular Sampled Time Series

1 code implementation • 14 Jan 2024 • Zhihao Yu, Xu Chu, Liantao Ma, Yasha Wang, Wenwu Zhu

To bridge this gap, we propose PRIME, a Prototype Recurrent Imputation ModEl, which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series.

Imputation Memorization +1

Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting

1 code implementation • 28 Dec 2023 • Zhihao Yu, Liantao Ma, Yasha Wang, Junfeng Zhao

In particular, a hierarchical convolution structure is introduced to extract the information from the series at various scales.

Denoising

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.

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

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