activity
20182024
most citedConditional Generation Net for Medication Recommendation

118 citations · 136 across the 5 of their papers we have counts for

collaborators

7 papers

cs.IR2024

A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation

Qidong Liu, Zhaopeng Qiu, Xiangyu Zhao +4

Medication recommendation is one of the most critical health-related applications, which has attracted extensive research interest recently. Most existing works focus on a single h…

eess.IV202218 cited

DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis

Xian Wu, Shuxin Yang, Zhaopeng Qiu +6

Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important…

cs.IR2022

Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback

Yunfan Hu, Zhaopeng Qiu, Xian Wu

News recommendation is different from movie or e-commercial recommendation as people usually do not grade the news. Therefore, user feedback for news is always implicit (click beha…

cs.LG2022118 cited

Conditional Generation Net for Medication Recommendation

Rui Wu, Zhaopeng Qiu, Jiacheng Jiang +2

Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics. Currently, the recommendation is manua…

cs.CL2020

Automatic Distractor Generation for Multiple Choice Questions in Standard Tests

Zhaopeng Qiu, Xian Wu, Wei Fan

To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests. However, the composition of the multiple choice qu…

cs.SI2018

Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

Jiangtao Wang, Feng Wang, Yasha Wang +3

Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper l…