Search Results for author: Zixiang Wang

Found 6 papers, 3 papers with code

REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models

no code implementations10 Feb 2024 Yinghao Zhu, Changyu Ren, Shiyun Xie, Shukai Liu, Hangyuan Ji, Zixiang Wang, Tao Sun, Long He, Zhoujun Li, Xi Zhu, Chengwei Pan

Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG).

Language Modelling Large Language Model +1

Learnable Prompt as Pseudo-Imputation: Reassessing the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction

no code implementations30 Jan 2024 Weibin Liao, Yinghao Zhu, Zixiang Wang, Xu Chu, Yasha Wang, Liantao Ma

PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all EHR analysis models.

Imputation

Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data

1 code implementation25 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.

Decision Making In-Context Learning

PRISM: Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration for EHR Data Sparsity Mitigation

1 code implementation8 Sep 2023 Yinghao Zhu, Zixiang Wang, Long He, Shiyun Xie, Liantao Ma, Chengwei Pan

Electronic Health Record (EHR) data, while rich in information, often suffers from sparsity, posing significant challenges in predictive modeling.

Imputation

Multilingual Entity and Relation Extraction from Unified to Language-specific Training

no code implementations11 Jan 2023 Zixiang Wang, Jian Yang, Tongliang Li, Jiaheng Liu, Ying Mo, Jiaqi Bai, Longtao He, Zhoujun Li

In this paper, we propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference across languages.

Relation Relation Extraction +1

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