activity
20182020
most citedMed-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction

60 citations · 61 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SE2020

Semantic based model of Conceptual Work Products for formal verification of complex interactive systems

Mohcine Madkour, Keith Butler, Eric Mercer +2

Many clinical workflows depend on interactive computer systems for highly technical, conceptual work products, such as diagnoses, treatment plans, care coordination, and case manag…

cs.CL202060 cited

Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction

Laila Rasmy, Yang Xiang, Ziqian Xie +2

Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often re…

cs.SI2019

Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States

Hansi Zhang, Christopher Wheldon, Adam G. Dunn +6

Objectives To test the feasibility of using Twitter data to assess determinants of consumers' health behavior towards Human papillomavirus (HPV) vaccination informed by the Integra…

cs.CL20191 cited

Exploring difference in public perceptions on HPV vaccine between gender groups from Twitter using deep learning

Jingcheng Du, Chongliang Luo, Qiang Wei +2

In this study, we proposed a convolutional neural network model for gender prediction using English Twitter text as input. Ensemble of proposed model achieved an accuracy at 0.8237…

cs.IR2018

ML-Net: multi-label classification of biomedical texts with deep neural networks

Jingcheng Du, Qingyu Chen, Yifan Peng +3

In multi-label text classification, each textual document can be assigned with one or more labels. Due to this nature, the multi-label text classification task is often considered…