N
Na Liu
Researcher at Max Planck Society
Publications - 184
Citations - 18121
Na Liu is an academic researcher from Max Planck Society. The author has contributed to research in topics: Medicine & DNA origami. The author has an hindex of 51, co-authored 130 publications receiving 14896 citations. Previous affiliations of Na Liu include University of Stuttgart & University of Mainz.
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DNA-Assembled Multilayer Sliding Nanosystems.
TL;DR: DNA-assembled multilayer nanosystems are demonstrated, which can carry out coordinated and reversible sliding motion powered by DNA fuels and seeds the basis to implement DNA-assembled complex optical nanoarchitectures with programmability and addressability, advancing the field with new momentum.
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Benchmarking emergency department prediction models with machine learning and public electronic health records
Feng Xie,Jun Zhou,Jin Wee Lee,Mi-duo Tan,Siqi Li,Logasan SO Rajnthern,Marcel Lucas Chee,Bibhas Chakraborty,A Ian Wong,Alon Dagan,Marcus Ong,Fei Gao,Na Liu +12 more
TL;DR: In this article , the authors proposed a clinical prediction benchmark for emergency care based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database.
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A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study
Yilin Ning,Siqi Li,Marcus Eng Hock Ong,Feng Xie,Bibhas Chakraborty,Daniel Shu Wei Ting,Na Liu +6 more
TL;DR: A robust and interpretable variable selection approach using the recently developed Shapley variable importance cloud (ShapleyVIC) that accounts for variability in variable importance across models is proposed, providing a disciplined solution to detailed assessment of variable importance and transparent development of parsimonious clinical risk scores.
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Longitudinal capacitance design for optical left-handed metamaterials
TL;DR: In this paper, the authors proposed a novel approach to design the longitudinal capacitance based on a metallic inductive/capacitive grating structure formed on a ridge patterned dielectric substrate.
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Predicting hospital emergency department visits with deep learning approaches
TL;DR: In this article , a deep stacked architecture is proposed and applied to the daily ED visits prediction problem with deep components such as Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), and simple Recurrent Neural Network (RNN).