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Yuqing Wang

Stanford University

18 papers hereh-index 11437 citations18 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author13
  • middle author5

Across the 18 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.LG6
  • cs.CV2
  • cs.AI1
affiliations
  • Stanford University
  • University of California, Santa Babrara
same name
  • Yuqing Wang — 14 papers, h 7
  • Yuqing Wang — 11 papers, h 4
  • Yuqing Wang — 10 papers, h 5
  • Yuqing Wang — 6 papers, h 6
  • Yuqing Wang — 6 papers, h 6
  • Yuqing Wang — 5 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212024
most citedAre Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding

29 citations · 92 across the 18 of their papers we have counts for

collaborators
Showing 2022Show all

3 papers · 1 filter

cs.LG2022★ 2 cited

Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning

Yuqing Wang, Yun Zhao, Linda Petzold

As critically ill patients frequently develop anemia or coagulopathy, transfusion of blood products is a frequent intervention in the Intensive Care Units (ICU). However, inappropr…

cs.LG2022★ 1 cited

Enhancing Transformer Efficiency for Multivariate Time Series Classification

Yuqing Wang, Yun Zhao, Linda Petzold

Most current multivariate time series (MTS) classification algorithms focus on improving the predictive accuracy. However, for large-scale (either high-dimensional or long-sequenti…

cs.LG2022★ 6 cited

Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis

Yuqing Wang, Yun Zhao, Rachael Callcut +1

Sepsis is a leading cause of death in the Intensive Care Units (ICU). Early detection of sepsis is critical for patient survival. In this paper, we propose a multimodal Transformer…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.