activity
20172021
most citedOutcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention

8 citations · 24 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG20212 cited

Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records

Yiqin Yu, Pin-Yu Chen, Yuan Zhou +1

With the successful adoption of machine learning on electronic health records (EHRs), numerous computational models have been deployed to address a variety of clinical problems. Ho…

cs.LG20201 cited

Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning

Yiqin Yu, Xu Min, Shiwan Zhao +5

In real world applications like healthcare, it is usually difficult to build a machine learning prediction model that works universally well across different institutions. At the s…

cs.CL20207 cited

Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification

Tengteng Zhang, Yiqin Yu, Jing Mei +3

The PICO framework (Population, Intervention, Comparison, and Outcome) is usually used to formulate evidence in the medical domain. The major task of PICO extraction is to extract…

q-bio.QM20198 cited

Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention

Eryu Xia, Xin Du, Jing Mei +8

Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in dat…

q-bio.QM20191 cited

From Risk Prediction Models to Risk Assessment Service: A Formulation of Development Paradigm

Eryu Xia, Yiqin Yu, Enliang Xu +2

Risk assessment services fulfil the task of generating a risk report from personal information and are developed for purposes like disease prognosis, resource utilization prioritiz…

cs.LG2018

Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks

Jing Mei, Shiwan Zhao, Feng Jin +3

In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention. EHR data consist of a sequence of medical visits, i.e. a multivar…