IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Models

1 code implementation • 23 Aug 2024 • Zhihao Yu, Yujie Jin, Yongxin Xu, Xu Chu, Yasha Wang, Junfeng Zhao

While pioneering deep learning methods have made great strides in analyzing electronic health record (EHR) data, they often struggle to fully capture the semantics of diverse medical codes from limited data.

SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction

1 code implementation • 15 May 2024 • Zhihao Yu, Xu Chu, Yujie Jin, Yasha Wang, Junfeng Zhao

To tackle this problem, we propose SMART, a Self-Supervised Missing-Aware RepresenTation Learning approach for patient health status prediction, which encodes missing information via elaborated attentions and learns to impute missing values through a novel self-supervised pre-training approach that reconstructs missing data representations in the latent space.

Imputation Missing Values +1

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 +2

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