Search Results for author: Yasha Wang

Found 25 papers, 14 papers with code

Distance Metric Learning with Joint Representation Diversification

1 code implementation ICML 2020 Xu Chu, Yang Lin, Xiting Wang, Xin Gao, Qi Tong, Hailong Yu, Yasha Wang

Distance metric learning (DML) is to learn a representation space equipped with a metric, such that examples from the same class are closer than examples from different classes with respect to the metric.

Metric Learning

LoRA Dropout as a Sparsity Regularizer for Overfitting Control

no code implementations15 Apr 2024 Yang Lin, Xinyu Ma, Xu Chu, Yujie Jin, Zhibang Yang, Yasha Wang, Hong Mei

We then demonstrate the theoretical mechanism of our LoRA Dropout mechanism from the perspective of sparsity regularization by providing a generalization error bound under this framework.

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.


Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation

1 code implementation18 Jan 2024 Ruizhe Zhang, Xinke Jiang, Yuchen Fang, Jiayuan Luo, Yongxin Xu, Yichen Zhu, Xu Chu, Junfeng Zhao, Yasha Wang

Graph Neural Networks (GNNs) have shown considerable effectiveness in a variety of graph learning tasks, particularly those based on the message-passing approach in recent years.

Graph Learning Node Classification

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

1 code implementation14 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 implementation28 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.


HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses

no code implementations26 Dec 2023 Xinke Jiang, Ruizhe Zhang, Yongxin Xu, Rihong Qiu, Yue Fang, Zhiyuan Wang, Jinyi Tang, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang

In this paper, we investigate the retrieval-augmented generation (RAG) based on Knowledge Graphs (KGs) to improve the accuracy and reliability of Large Language Models (LLMs).

Knowledge Graphs Retrieval

Predict and Interpret Health Risk using EHR through Typical Patients

1 code implementation18 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.

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

no code implementations11 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

A ModelOps-based Framework for Intelligent Medical Knowledge Extraction

no code implementations4 Oct 2023 Hongxin Ding, Peinie Zou, Zhiyuan Wang, Junfeng Zhao, Yasha Wang, Qiang Zhou

Extracting medical knowledge from healthcare texts enhances downstream tasks like medical knowledge graph construction and clinical decision-making.

Decision Making graph construction +2

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

1 code implementation28 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.

Domain Generalization through the Lens of Angular Invariance

1 code implementation28 Oct 2022 Yujie Jin, Xu Chu, Yasha Wang, Wenwu Zhu

Based on the proposed term of invariance, we propose a novel deep DG method called Angular Invariance Domain Generalization Network (AIDGN).

Domain Generalization Representation Learning

A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care

3 code implementations16 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.

Benchmarking Length-of-Stay prediction +1

MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering

no code implementations21 Apr 2022 Xinyu Ma, Xu Chu, Yasha Wang, Hailong Yu, Liantao Ma, Wen Tang, Junfeng Zhao

Thus, to address the issues, we expect to group up strongly correlated features and learn feature correlations in a group-wise manner to reduce the learning complexity without losing generality.

Clustering Representation Learning

StageNet: Stage-Aware Neural Networks for Health Risk Prediction

1 code implementation24 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.

ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context

1 code implementation27 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 implementation27 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

MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones

1 code implementation18 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.

Management Multi-Task Learning +1

Multi-Label Robust Factorization Autoencoder and its Application in Predicting Drug-Drug Interactions

no code implementations1 Nov 2018 Xu Chu, Yang Lin, Jingyue Gao, Jiangtao Wang, Yasha Wang, Leye Wang

However, the shallow models leveraging bilinear forms suffer from limitations on capturing complicated nonlinear interactions between drug pairs.

Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing

no code implementations19 Apr 2018 Leye Wang, wenbin liu, Daqing Zhang, Yasha Wang, En Wang, Yongjian Yang

Since the sensed data from different cells (sub-areas) of the target sensing area will probably lead to diverse levels of inference data quality, cell selection (i. e., choose which cells of the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i. e., data collection costs) for ensuring a certain level of quality.

reinforcement-learning Reinforcement Learning (RL) +1

Motif-based Rule Discovery for Predicting Real-valued Time Series

no code implementations14 Sep 2017 Yuanduo He, Xu Chu, Juguang Peng, Jingyue Gao, Yasha Wang

Time series prediction is of great significance in many applications and has attracted extensive attention from the data mining community.

Time Series Time Series Prediction +1

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